Datasets:

zurlog

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subset_wlabels

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10.1101/2020.07.02.20136721

10.1101/2020.07.02.20136721

Yes

2020-07-05

Preprint

medRxiv

an automatic computer-based method for fast and accurate covid-19 diagnosis

At present, the whole world is witnessing a horrifying outbreak caused by the Coronavirus Disease 2019 (COVID-19). The virus responsible for this disease is called SARS-CoV-2. It affects its victims’ respiratory system and causes severe lung inflammation, making it harder for them to breathe. The virus is airborne, and so has a high infection rate. Originated in China last December, the virus has spread across seven continents, affecting the population of over 210 countries, making it one of the fiercest pandemics ever recorded. Despite multiple independent and collaborative attempts to develop a vaccine or a cure, an effective solution is yet to come out. While the disease has put the world in a standstill, detecting the positive subjects and isolating them from the others as soon as possible is the only way to minimize its spread. However, many countries are currently experiencing a massive shortage of diagnostic equipment and medical personals. This insufficiency inspired us to work on a computer-based automatic method for the diagnosis of COVID-19. In this paper, we proposed a sequential Convolutional Neural Network (CNN)-based model to detect COVID-19 through analyzing Computed Tomography (CT) scan images. The model is capable of identifying the disease with almost 92.5% accuracy. We believe the implementation of this model will help the physicians and pathologists all over the world to single out the victims quickly and thus reduce the prevalence of COVID-19.

COVID-19;Infections;Pneumonitis

Disease Outbreaks;Other Topics

External

2. Detection/Diagnosis

CT

33169117

10.1016/j.ibmed.2020.100013

Yes

PMC7641591

2020-11-11

Journal Article

Peer reviewed (PubMed)

deep learning and its role in covid-19 medical imaging

COVID-19 is one of the greatest global public health challenges in history. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is estimated to have an cumulative global case-fatality rate as high as 7.2% (Onder et al., 2020) . As the SARS-CoV-2 spread across the globe it catalyzed new urgency in building systems to allow rapid sharing and dissemination of data between international healthcare infrastructures and governments in a worldwide effort focused on case tracking/tracing, identifying effective therapeutic protocols, securing healthcare resources, and in drug and vaccine research. In addition to the worldwide efforts to share clinical and routine population health data, there are many large-scale efforts to collect and disseminate medical imaging data, owing to the critical role that imaging has played in diagnosis and management around the world. Given reported false negative rates of the reverse transcriptase polymerase chain reaction (RT-PCR) of up to 61% (Centers for Disease Control and Prevention, Division of Viral Diseases, 2020; Kucirka et al., 2020) , imaging can be used as an important adjunct or alternative. Furthermore, there has been a shortage of test-kits worldwide and laboratories in many testing sites have struggled to process the available tests within a reasonable time frame. Given these issues surrounding COVID-19, many groups began to explore the benefits of 'big data' processing and algorithms to assist with the diagnosis and therapeutic development of COVID-19.

COVID-19;Severe Acute Respiratory Syndrome;Virus Diseases

Intell Based Med

Public Health;Health Care;Polymerase Chain Reaction;Other Topics;Pharmaceutical Preparations

N.A.

Review

Multimodal

2008.08840

Yes

2021-01-18

Preprint

arXiv

image quality assessment for closed-loop computer-assisted lung ultrasound

We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-oh-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training the quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Using more than 25000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient quality - as determined by the quality assessment module - the mean classification accuracy, sensitivity, and specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97, respectively, across five holdout test data sets unseen during the training of any networks within the proposed system. Overall, the integration of the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at point-of-care.

COVID-19

Point-of-Care Systems;Ultrasonography

Self-recorded/clinical

2. Detection/Diagnosis

Ultrasound

32496988

10.2174/1573405616666200604163954

Yes

2020-06-05

Journal Article

Peer reviewed (PubMed)

a deep neural network to distinguish covid-19 from other chest diseases using x-ray images

Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidisciplinary approach is strongly required to confirm the diagnosis. There are only a few works targeted at pathological x-ray images. Most of the works only target single disease detection which is not good enough. Some works have been provided for all classes. However, the results suffer due to lack of data for rare classes and data unbalancing problem. Due to the rise in COVID-19 cases, medical facilities in many countries are overwhelmed and there is a need for an intelligent system to detect it. Few works have been done regarding the detection of the coronavirus but there are many cases where it can be misclassified as some techniques are not efficient and can only identify specific diseases. This work is a deep learning- based model to distinguish COVID-19 cases from other chest diseases. A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides an effective analysis of chest-related diseases taking into account both age and gender. Our model achieves 87% accuracy in terms of GAN-based synthetic data and presents four different types of deep learning-based models that provide comparable results to other state-of-the-art techniques. The healthcare industry may face unfavorable consequences if the gap in the identification of all types of pneumonia is not filled with effective automation.

COVID-19;Pneumonia

Curr Med Imaging

Art;Health Care;Neural Networks;Other Topics;Lung Diseases

External

2. Detection/Diagnosis

X-Ray

34038371

10.1109/TNNLS.2021.3082015

Yes

2021-05-27

Journal Article

Peer reviewed (PubMed)

4s-dt: self-supervised super sample decomposition for transfer learning with application to covid-19 detection

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this article, we propose a novel deep convolutional neural network, which we called self-supervised super sample decomposition for transfer learning (4S-DT) model. The 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabeled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition (CD) layer to simplify the local structure of the data. The 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream CD mechanism. We used 50000 unlabeled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. The 4S-DT has achieved a high accuracy of 99.8% on the larger of the two datasets used in the experimental study and an accuracy of 97.54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected.

COVID-19

IEEE Trans Neural Netw Learn Syst

Reproducibility of Results;Algorithms;Transfer Learning;Neural Networks;Other Topics;ROC Curve

External

2. Detection/Diagnosis

X-Ray

33125051

10.1093/jamia/ocaa280

Yes

PMC7665533

2020-10-31

Journal Article

Peer reviewed (PubMed)

flannel: focal loss based neural network ensemble for covid-19 detection

The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumonia and healthy patients using deep neural networks. We construct the radiography (x-ray) imaging data from 2 publicly available sources, which include 5508 chest x-ray images across 2874 patients with 4 classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a FLANNEL (Focal Loss bAsed Neural Network EnsembLe) model, a flexible module to ensemble several convolutional neural network models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score, with 6% relative increase on the COVID-19 identification task, in which it achieves precision of 0.7833 , recall of 0.8609 , and F1 score of 0.8168 . Ensemble learning that combines multiple independent basis classifiers can increase the robustness and accuracy. We propose a neural weighing module to learn the importance weight for each base model and combine them via weighted ensemble to get the final classification results. In order to handle the class imbalance challenge, we adapt focal loss to our multiple classification task as the loss function. FLANNEL effectively combines state-of-the-art convolutional neural network classification models and tackles class imbalance with focal loss to achieve better performance on COVID-19 detection from x-rays.

COVID-19;Pneumonia;Pneumonia, Bacterial;Pneumonia, Viral

J Am Med Inform Assoc

Art;Algorithms;Neural Networks;Other Topics;ROC Curve

External

2. Detection/Diagnosis

X-Ray

32864270

10.7759/cureus.9448

Yes

PMC7451075

2020-08-31

Journal Article

Peer reviewed (PubMed)

predicting covid-19 pneumonia severity on chest x-ray with deep learning

The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pre-trained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model's ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online.

COVID-19;Infections;Pneumonia

Cureus

Neural Networks;Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

2009.06412

Yes

2022-05-16

Preprint

arXiv

comprehensive comparison of deep learning models for lung and covid-19 lesion segmentation in ct scans

Recently there has been an explosion in the use of Deep Learning (DL) methods for medical image segmentation. However the field's reliability is hindered by the lack of a common base of reference for accuracy/performance evaluation and the fact that previous research uses different datasets for evaluation. In this paper, an extensive comparison of DL models for lung and COVID-19 lesion segmentation in Computerized Tomography (CT) scans is presented, which can also be used as a benchmark for testing medical image segmentation models. Four DL architectures (Unet, Linknet, FPN, PSPNet) are combined with 25 randomly initialized and pretrained encoders (variations of VGG, DenseNet, ResNet, ResNext, DPN, MobileNet, Xception, Inception-v4, EfficientNet), to construct 200 tested models. Three experimental setups are conducted for lung segmentation, lesion segmentation and lesion segmentation using the original lung masks. A public COVID-19 dataset with 100 CT scan images (80 for train, 20 for validation) is used for training/validation and a different public dataset consisting of 829 images from 9 CT scan volumes for testing. Multiple findings are provided including the best architecture-encoder models for each experiment as well as mean Dice results for each experiment, architecture and encoder independently. Finally, the upper bounds improvements when using lung masks as a preprocessing step or when using pretrained models are quantified. The source code and 600 pretrained models for the three experiments are provided, suitable for fine-tuning in experimental setups without GPU capabilities.

COVID-19

Other Topics

External

Segmentation-only

CT

10.1101/2020.08.18.20175521

10.1101/2020.08.18.20175521

Yes

2020-08-21

Preprint

medRxiv

machine learning and ai aided tool to differentiate covid-19 and non-covid-19 lung cxr

One of the main challenges in dealing with the current COVID 19 pandemic is how to detect and distinguish between the COVID 19 and non COVID 19 cases. This problem arises since COVID 19 symptoms resemble with other cases. One of the golden standards is by examining the lung using the chest X ray radiograph (CXR). Currently there is growing COVID 19 cases followed by the CXR images waiting to be analyzed and this may outnumber the health capacity. Learning from that current situation and to fulfill the demand for CXRs analysis, a novel solution is required. The tool is expected can detect and distinguish the COVID 19 case lung rely on CXR. Respectively, this study aims to propose the use of AI and machine learning aided tool to distinguish the COVID 19 and non COVID 19 cases based on the CXR lung image. The compared non COVID 19 CXR cases in this study include normal (healthy), influenza A, tuberculosis, and active smoker. The results confirm that the machine learning tool is able to distinguish the COVID 19 CXR lungs based on lung consolidation. Moreover, the tool is also able to recognize an abnormality of COVID 19 lung in the form of patchy ground glass opacity. To conclude, AI and machine learning may be considered as a detection tool to identify and distinguish between COVID 19 and non COVID 19 cases in particular epidemic areas.

COVID-19;COVID-19 Pandemic;Influenza, Human;Tuberculosis

Health;Eyeglasses

External

2. Detection/Diagnosis

X-Ray

2005.02167

Yes

2020-04-30

Preprint

arXiv

intra-model variability in covid-19 classification using chest x-ray images

X-ray and computed tomography (CT) scanning technologies for COVID-19 screening have gained significant traction in AI research since the start of the coronavirus pandemic. Despite these continuous advancements for COVID-19 screening, many concerns remain about model reliability when used in a clinical setting. Much has been published, but with limited transparency in expected model performance. We set out to address this limitation through a set of experiments to quantify baseline performance metrics and variability for COVID-19 detection in chest x-ray for 12 common deep learning architectures. Specifically, we adopted an experimental paradigm controlling for train-validation-test split and model architecture where the source of prediction variability originates from model weight initialization, random data augmentation transformations, and batch shuffling. Each model architecture was trained 5 separate times on identical train-validation-test splits of a publicly available x-ray image dataset provided by Cohen et al. . Results indicate that even within model architectures, model behavior varies in a meaningful way between trained models. Best performing models achieve a false negative rate of 3 out of 20 for detecting COVID-19 in a hold-out set. While these results show promise in using AI for COVID-19 screening, they further support the urgent need for diverse medical imaging datasets for model training in a way that yields consistent prediction outcomes. It is our hope that these modeling results accelerate work in building a more robust dataset and a viable screening tool for COVID-19.

COVID-19

Other Topics

External

2. Detection/Diagnosis

X-Ray

2007.04774

10.1016/j.imu.2021.100681.

Yes

2020-06-24

Preprint

arXiv

automated chest ct image segmentation of covid-19 lung infection based on 3d u-net

The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of RT-PCR as screening method, medical imaging like computed tomography offers great potential as alternative. For this reason, automated image segmentation is highly desired as clinical decision support for quantitative assessment and disease monitoring. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures. Through a 5-fold cross-validation on 20 CT scans of COVID-19 patients, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on the limited data. Our method achieved Dice similarity coefficients of 0.956 for lungs and 0.761 for infection. We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves medical image analysis with limited data. The code and model are available under the following link: GitHub

COVID-19;Infections

Art;Health Care;Architecture;Polymerase Chain Reaction;Tomography;Other Topics;Lung Diseases

External

Segmentation-only

CT

32773400

10.3233/XST-200715

Yes

PMC7592691

2020-08-11

Comparative Study;Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

identification of covid-19 samples from chest x-ray images using deep learning: a comparison of transfer learning approaches

The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

COVID-19;COVID-19 Pandemic;Death;Pneumonia

J Xray Sci Technol

Coronavirus Infections;Public Health;Health Care;Transfer Learning;Algorithms;Neural Networks;Health Care Systems;Tomography

External

2. Detection/Diagnosis

X-Ray

32843849

10.1016/j.procbio.2020.08.016

Yes

PMC7439988

2020-08-28

Journal Article;Review

Peer reviewed (PubMed)

a systematic review on recent trends in transmission diagnosis prevention and imaging features of covid-19

As the new cases of COVID-19 are growing every daysince January 2020, the major way to control the spread wasthrough early diagnosis. Prevention and early diagnosis are the key strategies followed by most countries. This study presents the perspective of different modes of transmission of coronavirus,especially during clinical practices and among the pediatrics. Further, the diagnostic methods and the advancement of the computerized tomography have been discussed. Droplets, aerosol, and close contact are thesignificantfactors to transfer the infection to the suspect. This study predicts the possible transmission of the virus through medical practices such as ophthalmology, dental, and endoscopy procedures. With regard to pediatric transmission, as of now, only afew child fatalities had been reported. Childrenusually respond to the respiratory virus; however, COVID-19 response ison the contrary. The possibility of getting infected is minimal for the newborn. There has been no asymptomatic spread in children until now. Moreover, breastfeedingwould not transmit COVID-19, which is encouraging hygiene news for the pediatric. In addition, the current diagnostic methods for COVID-19 including Immunoglobulin M (IgM) and Immunoglobulin G (IgG)and chest computed topography (CT) scan, reverse transcription-polymerase chain reaction (RT-PCR) andimmunochromatographic fluorescence assay, are also discussed in detail. The introduction of artificial intelligence and deep learning algorithmhas the ability to diagnose COVID-19 in precise. However, the developments of a potential technology for the identification of the infection, such as a drone with thermal screening without human intervention, need to be encouraged.

COVID-19;Infections

Process Biochem

Coronavirus Infections;Systematic Review;Polymerase Chain Reaction;Tomography;Reverse Transcription

N.A.

Review

Multimodal

32958781

10.1038/s41598-020-71294-2

Yes

PMC7506559

2020-09-23

Journal Article

Peer reviewed (PubMed)

covid-19 image classification using deep features and fractional-order marine predators algorithm

Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.

COVID-19;Calculi;Death

Sci Rep

Coronavirus Infections;Art;Algorithms;Architecture;Research Personnel;Image Processing;Neural Networks

External

2. Detection/Diagnosis

X-Ray

2009.10141

10.1007/s42600-020-00110-7

Yes

2020-09-21

Preprint

arXiv

ccblock: an effective use of deep learning for automatic diagnosis of covid-19 using x-ray images

Troubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world's population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis. : Based on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. The model testing dataset included 1,828 x-ray images available on public platforms. 310 images were showing confirmed COVID-19 cases, 864 images indicating pneumonia cases, and 654 images showing healthy people. According to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes. According to the results, using the enhanced VGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography.

COVID-19;COVID-19 Pandemic;Pneumonia

Polymerase Chain Reaction;Other Topics

External

2. Detection/Diagnosis

X-Ray

33144676

10.1038/s41598-020-76141-y

Yes

PMC7641115

2020-11-05

Journal Article

Peer reviewed (PubMed)

the study of automatic machine learning base on radiomics of non-focus area in the first chest ct of different clinical types of covid-19 pneumonia

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve (AUC), true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.

COVID-19;Pneumonia;Virus Diseases

Sci Rep

Coronavirus Infections;ROC Curve;Lung Diseases;Age

Self-recorded/clinical

2. Detection/Diagnosis

CT

32524445

10.1007/s13246-020-00865-4

Yes

PMC7118364

2020-06-12

Journal Article

Peer reviewed (PubMed)

covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks

In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

COVID-19;Pneumonia, Bacterial;Pneumonia, Viral

Phys Eng Sci Med

Radiography;Coronavirus Infections;Art;Transfer Learning;Diagnostic Tests;Architecture;COVID-19 Testing;Sensitivity and Specificity;Neural Networks

External

2. Detection/Diagnosis

X-Ray

32758014

10.1259/bjr.20200538

Yes

PMC7465853

2020-08-08

Journal Article;Review

Peer reviewed (PubMed)

imaging of covid-19 pneumonia: patterns pathogenesis and advances

COVID-19 pneumonia is a newly recognized lung infection. Initially, CT imaging was demonstrated to be one of the most sensitive tests for the detection of infection. Currently, with broader availability of polymerase chain reaction for disease diagnosis, CT is mainly used for the identification of complications and other defined clinical indications in hospitalized patients. Nonetheless, radiologists are interpreting lung imaging in unsuspected patients as well as in suspected patients with imaging obtained to rule out other relevant clinical indications. The knowledge of pathological findings is also crucial for imagers to better interpret various imaging findings. Identification of the imaging findings that are commonly seen with the disease is important to diagnose and suggest confirmatory testing in unsuspected cases. Proper precautionary measures will be important in such unsuspected patients to prevent further spread. In addition to understanding the imaging findings for the diagnosis of the disease, it is important to understand the growing set of tools provided by artificial intelligence. The goal of this review is to highlight common imaging findings using illustrative examples, describe the evolution of disease over time, discuss differences in imaging appearance of adult and pediatric patients and review the available literature on quantitative CT for COVID-19. We briefly address the known pathological findings of the COVID-19 lung disease that may help better understand the imaging appearance, and we provide a demonstration of novel display methodologies and artificial intelligence applications serving to support clinical observations.

COVID-19;Infections;Lung Diseases;Pneumonia

Br J Radiol

Coronavirus Infections;Polymerase Chain Reaction

N.A.

Review

Multimodal

33166256

10.1109/JBHI.2020.3036722

Yes

2020-11-10

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

classification of severe and critical covid-19 using deep learning and radiomics

The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. We retrospectively enrolled 217 patients from three centers in China, including 82 patients with severe disease and 135 with critical disease. Patients were randomly divided into a training cohort (n = 174) and a test cohort (n = 43). We extracted 102 3-dimensional radiomic features from automatically segmented lung volume and selected the significant features. We also developed a 3-dimensional DL network based on center-cropped slices. Using multivariable logistic regression, we then created a merged model based on significant radiomic features and DL scores. We employed the area under the receiver operating characteristic curve (AUC) to evaluate the model's performance. We then conducted cross validation, stratified analysis, survival analysis, and decision curve analysis to evaluate the robustness of our method. The merged model can distinguish critical patients with AUCs of 0.909 (95% confidence interval : 0.859-0.952) and 0.861 (95% CI: 0.753-0.968) in the training and test cohorts, respectively. Stratified analysis indicated that our model was not affected by sex, age, or chronic disease. Moreover, the results of the merged model showed a strong correlation with patient outcomes. A model combining radiomic and DL features of the lung could help distinguish critical cases from severe cases of COVID-19.

COVID-19;Chronic Disease

IEEE J Biomed Health Inform

Other Topics

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

32868956

10.1016/j.patcog.2020.107613

Yes

PMC7448783

2020-09-02

Journal Article

Peer reviewed (PubMed)

automatically discriminating and localizing covid-19 from community-acquired pneumonia on chest x-rays

The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.

COVID-19;Pneumonia

Pattern Recognit

Disease Outbreaks;Radiologists

External

2. Detection/Diagnosis

X-Ray

33718884

10.1007/s42979-021-00496-w

Yes

PMC7944725

2021-03-16

Journal Article

Peer reviewed (PubMed)

identification of images of covid-19 from chest x-rays using deep learning: comparing cognex visionpro deep learning 10 software with open source convolutional neural networks

The novel Coronavirus, COVID-19, pandemic is being considered the most crucial health calamity of the century. Many organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, The University of Waterloo, along with Darwin AI-a start-up spin-off of this department, has designed the Deep Learning model 'COVID-Net' and created a dataset called 'COVIDx' consisting of 13,975 images across 13,870 patient cases. In this study, COGNEX's Deep Learning Software, VisionPro Deep Learning, is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state-of-the-art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, first, by selecting the entire image as the Region of Interest (ROI), and second, by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results: on the entire image as the ROI it achieves an overall F score of 94.0%, and on the segmented lungs, it gets an F score of 95.3%, which is better than COVID-Net and other state-of-the-art open-source Deep Learning models.

COVID-19;COVID-19 Pandemic

SN Comput Sci

Black Americans;Art;Health;X-Rays;Dataset;Other Topics

External

2. Detection/Diagnosis

X-Ray

32666395

10.1007/s00259-020-04953-1

Yes

PMC7358997

2020-07-16

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

automated detection and quantification of covid-19 pneumonia: ct imaging analysis by a deep learning-based software

The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia. A total of 2460 RT-PCR tested SARS-CoV-2-positive patients (1250 men and 1210 women; mean age, 57.7 years (age range, 11-93 years) were retrospectively identified from Huoshenshan Hospital in Wuhan from February 11 to March 16, 2020. Basic clinical characteristics were reviewed. The uAI Intelligent Assistant Analysis System was used to assess the CT scans. CT scans of 2215 patients showed multiple lesions of which 36 and 50 patients had left and right lung infections, respectively (> 50% of each affected lung's volume), while 27 had total lung infection (> 50% of the total volume of both lungs). Overall, 298 and 1300 patients exhibited pure ground glass opacities (GGOs), GGOs with sub-solid lesions and GGOs with both sub-solid and solid lesions, respectively. Moreover, 2305 and 71 patients presented primarily with GGOs and sub-solid lesions, respectively. Elderly patients (≥ 60 years) were more likely to exhibit sub-solid lesions. The generalized linear mixed model showed that the dorsal segment of the right lower lobe was the favoured site of COVID-19 pneumonia. Chest CT combined with analysis by the uAI Intelligent Assistant Analysis System can accurately evaluate pneumonia in COVID-19 patients.

COVID-19;Infections;Pneumonia

Eur J Nucl Med Mol Imaging

Coronavirus Infections;Public Health;COVID-19 Testing;Polymerase Chain Reaction;Retrospective Studies

Self-recorded/clinical

2. Detection/Diagnosis

CT

34786299

10.1109/ACCESS.2020.3044858

Yes

PMC8545248

2021-11-18

Journal Article

Peer reviewed (PubMed)

artificial intelligence applied to chest x-ray images for the automatic detection of covid-19 a thoughtful evaluation approach

Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests and provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity. With the long term goal to provide new evidence for the diagnosis, this paper presents an evaluation of different methods based on a deep neural network. These are the first steps to develop an automatic COVID-19 diagnosis tool using chest X-Ray images to differentiate between controls, pneumonia, or COVID-19 groups. The paper describes the process followed to train a Convolutional Neural Network with a dataset of more than 79, 500 X-Ray images compiled from different sources, including more than 8, 500 COVID-19 examples. Three different experiments following three preprocessing schemes are carried out to evaluate and compare the developed models. The aim is to evaluate how preprocessing the data affects the results and improves its explainability. Likewise, a critical analysis of different variability issues that might compromise the system and its effects is performed. With the employed methodology, a 91.5% classification accuracy is obtained, with an 87.4% average recall for the worst but most explainable experiment, which requires a previous automatic segmentation of the lung region.

COVID-19;Pneumonia

IEEE Access

Other Topics

External

2. Detection/Diagnosis

X-Ray

32982615

10.1016/j.asoc.2020.106742

Yes

PMC7505822

2020-09-29

Journal Article

Peer reviewed (PubMed)

an optimized deep learning architecture for the diagnosis of covid-19 disease based on gravitational search optimization

In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.

COVID-19

Appl Soft Comput

Algorithms;Transfer Learning;Architecture

External

2. Detection/Diagnosis

X-Ray

2006.13262

Yes

2020-06-23

Preprint

arXiv

was there covid-19 back in 2012? challenge for ai in diagnosis with similar indications

Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the disease. To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability. In this paper, we present our argument regarding the efficiency and applicability of existing deep learning models for COVID-19 diagnosis. We provide results from two popular models - COVID-Net and CoroNet evaluated on three publicly available datasets and an additional institutional dataset collected from EMORY Hospital between January and May 2020, containing patients tested for COVID-19 infection using RT-PCR. There is a large false positive rate (FPR) for COVID-Net on both ChexPert and MIMIC-CXR dataset. On the EMORY Dataset, COVID-Net has 61.4% sensitivity, 0.54 F1-score and 0.49 precision value. The FPR of the CoroNet model is significantly lower across all the datasets as compared to COVID-Net - EMORY, ChexPert , ChestX-ray14 , MIMIC-CXR . The models reported good to excellent performance on their internal datasets, however we observed from our testing that their performance dramatically worsened on external data. This is likely from several causes including overfitting models due to lack of appropriate control patients and ground truth labels. The fourth institutional dataset was labeled using RT-PCR, which could be positive without radiographic findings and vice versa. Therefore, a fusion model of both clinical and radiographic data may have better performance and generalization.

COVID-19;Infections

Disease Outbreaks;Polymerase Chain Reaction;Other Topics

External

2. Detection/Diagnosis

X-Ray

32555006

10.1097/RTI.0000000000000544

Yes

PMC7682797

2020-06-20

Journal Article

Peer reviewed (PubMed)

a novel machine learning-derived radiomic signature of the whole lung differentiates stable from progressive covid-19 infection: a retrospective cohort study

This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection. The clinical and imaging data of 64 patients with confirmed diagnoses of COVID-19 were retrospectively selected and divided into a stable group and a progressive group according to the data obtained from the ongoing treatment process. Imaging features from whole-lung images from baseline computed tomography (CT) scans were extracted and dimensionality reduction was performed. Support vector machines were used to construct radiomics signatures and to compare differences between the 2 groups. We also compared the differences of signature scores in the clinical, laboratory, and CT image feature subgroups and finally analyzed the correlation between the radiomics features of the constructed signature and the other features including clinical, laboratory, and CT imaging features. The signature has a good classification effect for the stable group and the progressive group, with area under curve, sensitivity, and specificity of 0.833, 80.95%, and 74.42%, respectively. Signature score differences in laboratory and CT imaging features between subgroups were not statistically significant (P>0.05); cough was negatively correlated with GLCM Entropy_angle 90_offset4 (r=-0.578), but was positively correlated with ShortRunEmphhasis_AllDirect_offset4_SD (r=0.454); C-reactive protein was positively correlated with Cluster Prominence_ AllDirect_offset 4_ SD (r=0.47). The radiomics signature of the whole lung based on machine learning may reveal the changes of lung microstructure in the early stage and help to indicate the progression of the disease.

COVID-19;Cough;Infections

J Thorac Imaging

Severity of Illness Index;Sensitivity;C-Reactive Protein;Humans;Retrospective Studies;Entropy;Support Vector Machine;Area under Curve;Age

External

4. Prognosis/Treatment

CT

34108787

10.1016/j.bbe.2021.05.013

Yes

PMC8179118

2021-06-11

Journal Article

Peer reviewed (PubMed)

automatic detection of coronavirus disease (covid-19) in x-ray and ct images: a machine learning-based approach

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98.

COVID-19;Cough;Fever;Pneumonia;Pulmonary Edema;Respiratory Distress Syndrome, Acute;Shock, Septic;Sore Throat

Biocybern Biomed Eng

Transfer Learning;Lung;Antiviral Agents;Pharmaceutical Preparations

External

2. Detection/Diagnosis

X-Ray

33025386

10.1007/s13246-020-00934-8

Yes

PMC7537970

2020-10-08

Journal Article

Peer reviewed (PubMed)

issues associated with deploying cnn transfer learning to detect covid-19 from chest x-rays

Covid-19 first occurred in Wuhan, China in December 2019. Subsequently, the virus spread throughout the world and as of June 2020 the total number of confirmed cases are above 4.7 million with over 315,000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To accomplish this task, we use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images are fed into CNN models without any preprocessing to replicate researches used chest X-rays in this manner. Then a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed to the decision of CNNs back to the original image to visualize the most discriminating regions on the input image. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect and approve the regions of the input image used by CNNs that lead to its prediction.

COVID-19;Death

Phys Eng Sci Med

Algorithms;Transfer Learning;Architecture;Image Processing;Neural Networks;Map

External

2. Detection/Diagnosis

X-Ray

33230395

10.1016/j.asoc.2020.106912

Yes

PMC7673219

2020-11-25

Journal Article

Peer reviewed (PubMed)

cnn-based transfer learning-bilstm network: a novel approach for covid-19 infection detection

Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success.

COVID-19;Death;Infections

Appl Soft Comput

Transfer Learning;Architecture;Lung;Polymerase Chain Reaction;Tomography;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

2008.06330

Yes

2020-08-13

Preprint

arXiv

automated detection and quantification of covid-19 airspace disease on chest radiographs: a novel approach achieving radiologist-level performance using a cnn trained on digital reconstructed radiographs (drrs) from ct-based ground-truth

To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. : We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT and CXR within 48 hrs. The ground truth volumetric percentage of COVID-19 related AD (POv) was established by manual AD segmentation on CT. The resulting 3D masks were projected into 2D anterior-posterior digitally reconstructed radiographs (DRR) to compute area-based AD percentage (POa). A convolutional neural network (CNN) was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD and quantifying POa on CXR. CNN POa results were compared to POa quantified on CXR by two expert readers and to the POv ground-truth, by computing correlations and mean absolute errors. Bootstrap mean absolute error (MAE) and correlations between POa and POv were 11.98% and 0.77 for average of expert readers, and 9.56%-9.78% and 0.78-0.81 for the CNN, respectively. Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of airspace disease on CXR, in patients with positive RT-PCR for COVID-19.

COVID-19

Polymerase Chain Reaction;Other Topics

Self-recorded/clinical

3. Monitoring/Severity assessment

Multimodal

10.1101/2020.09.07.20189852

10.1101/2020.09.07.20189852

Yes

2020-09-09

Preprint

medRxiv

network for subclinical prognostication of covid-19 patients from data of thoracic roentgenogram: a feasible alternative screening technology

COVID 19 is the terminology driving people’s life in the year 2020 without a supportive globally high mortality rate. Coronavirus lead pandemic is a new found disease with no gold standard diagnostic and therapeutic guideline across the globe. Amidst this scenario our aim is to develop a prediction model that makes mass screening easy on par with reducing strain on hospitals diagnostic facility and doctors alike. For this prediction model, a neural network based on Chest X-ray images has been developed. Alongside the aim is also to generate a case record form that would include prediction model result along with few other subclinical factors for generating disease identification. Once found positive then only it will proceed to RT-PCR for final validation. The objective was to provide a cheap alternative to RT-PCR for mass screening and to reduced burden on diagnostic facility by keeping RT-PCR only for final confirmation. Datasets of chest X-ray images gathered from across the globe has been used to test and train the network after proper dataset curing and augmentation. The final neural network-based prediction model showed an accuracy of 81% with sensitivity of 82% and specificity of 90%. The AUC score obtained is 93.7%. The above results based on the existing datasets showcase our model capability to successfully distinguish patients based on Chest X-ray (a non-invasive tool) and along with the designed case record form it can significantly contribute in increasing hospitals monitoring and health care capability.

COVID-19;Strains

Health Care;Sensitivity and Specificity;Polymerase Chain Reaction;Area under Curve

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.08.12.20173872

10.1101/2020.08.12.20173872

Yes

2020-08-14

Preprint

medRxiv

severity assessment of covid-19 based on clinical and imaging data

This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data. Clinical data, demographics, signs, symptoms, comorbidities and blood test results, and chest CT scans of 346 patients from two hospitals in the Hubei province, China, were used to develop machine learning models for automated severity assessment of diagnosed COVID-19 cases. We compared the predictive power of clinical and imaging data by testing multiple machine learning models, and further explored the use of four oversampling methods to address the imbalance distribution issue. Features with the highest predictive power were identified using the SHAP framework. Targeting differentiation between mild and severe cases, logistic regression models achieved the best performance on clinical features (AUC:0.848, sensitivity:0.455, specificity:0.906), imaging features (AUC:0.926, sensitivity:0.818, specificity:0.901) and the combined features (AUC:0.950, sensitivity:0.764, specificity:0.919). The SMOTE oversampling method further improved the performance of the combined features to AUC of 0.960 (sensitivity:0.845, specificity:0.929). Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with findings from previous studies. Oversampling yielded mixed results, although it achieved the best performance in our study. This study indicates that clinical and imaging features can be used for automated severity assessment of COVID-19 patients and have the potential to assist with triaging COVID-19 patients and prioritizing care for patients at higher risk of severe cases.

COVID-19

COVID-19 Testing;Hematologic Tests

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

2009.10401

Yes

2020-10-25

Preprint

arXiv

dynamic fusion based federated learning for covid-19 detection

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients' privacy. This causes the issue of insufficient datasets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received updates of local models trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces huge communication cost of transferring model updates and can hardly ensure model performance when data heterogeneity of clients heavily exists. To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyse medical diagnostic images. Further, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion-based on participating clients' training time. In addition, we summarise a category of medical diagnostic image datasets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency and fault tolerance.

COVID-19;Infections

Other Topics

External

2. Detection/Diagnosis

Multimodal

33180877

10.1371/journal.pone.0242301

Yes

PMC7660555

2020-11-13

Journal Article;Research Support, N.I.H., Intramural

Peer reviewed (PubMed)

analyzing inter-reader variability affecting deep ensemble learning for covid-19 detection in chest radiographs

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., adapting to visual characteristics that are unlike natural images; modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; challenges in explaining DL black-box behavior to support clinical decision-making; and inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; using ensembles of the fine-tuned models to further improve performance over individual constituent models; performing statistical analyses at various learning stages for validating results; interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.

COVID-19

PLoS One

Radiography;Coronavirus Infections;Black Americans;Noise;X-Rays;Image Processing;Neural Networks

External

2. Detection/Diagnosis

X-Ray

2011.11736

Yes

2021-01-08

Preprint

arXiv

accurate and rapid diagnosis of covid-19 pneumonia with batch effect removal of chest ct-scans and interpretable artificial intelligence

COVID-19 is a virus with high transmission rate that demands rapid identification of the infected patients to reduce the spread of the disease. The current gold-standard test, Reverse-Transcription Polymerase Chain Reaction (RT-PCR), has a high rate of false negatives. Diagnosing from CT-scan images as a more accurate alternative has the challenge of distinguishing COVID-19 from other pneumonia diseases. Artificial intelligence can help radiologists and physicians to accelerate the process of diagnosis, increase its accuracy, and measure the severity of the disease. We designed a new interpretable deep neural network to distinguish healthy people, patients with COVID-19, and patients with other pneumonia diseases from axial lung CT-scan images. Our model also detects the infected areas and calculates the percentage of the infected lung volume. We first preprocessed the images to eliminate the batch effects of different devices, and then adopted a weakly supervised method to train the model without having any tags for the infected parts. We trained and evaluated the model on a large dataset of 3359 samples from 6 different medical centers. The model reached sensitivities of 97.75% and 98.15%, and specificities of 87% and 81.03% in separating healthy people from the diseased and COVID-19 from other diseases, respectively. It also demonstrated similar performance for 1435 samples from 6 different medical centers which proves its generalizability. The performance of the model on a large diverse dataset, its generalizability, and interpretability makes it suitable to be used as a reliable diagnostic system.

COVID-19;Pneumonia

Polymerase Chain Reaction;Reverse Transcription

Self-recorded/clinical

2. Detection/Diagnosis

CT

2012.14106

Yes

2020-12-28

Preprint

arXiv

diagnosis/prognosis of covid-19 images: challenges opportunities and applications

The novel Coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew in 2020. It becomes the most unprecedent challenge to analytic epidemiology in general and signal processing theories in specific. Given its high contingency nature and adverse effects across the world, it is important to develop efficient processing/learning models to overcome this pandemic and be prepared for potential future ones. In this regard, medical imaging plays an important role for the management of COVID-19. Human-centered interpretation of medical images is, however, tedious and can be subjective. This has resulted in a surge of interest to develop Radiomics models for analysis and interpretation of medical images. Signal Processing (SP) and Deep Learning (DL) models can assist in development of robust Radiomics solutions for diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients. In this article, we aim to present an overview of the current state, challenges, and opportunities of developing SP/DL-empowered models for diagnosis (screening/monitoring) and prognosis (outcome prediction and severity assessment) of COVID-19 infection. More specifically, the article starts by elaborating the latest development on the theoretical framework of analytic epidemiology and hypersignal processing for COVID-19. Afterwards, imaging modalities and Radiological characteristics of COVID-19 are discussed. SL/DL-based Radiomic models specific to the analysis of COVID-19 infection are then described covering the following four domains: Segmentation of COVID-19 lesions; Predictive models for outcome prediction; Severity assessment, and; Diagnosis/classification models. Finally, open problems and opportunities are presented in detail.

COVID-19;Infections

Other Topics

N.A.

Review

Multimodal

32921934

10.1016/j.chaos.2020.110245

Yes

PMC7472981

2020-09-15

Journal Article

Peer reviewed (PubMed)

cvdnet: a novel deep learning architecture for detection of coronavirus (covid-19) from chest x-ray images

The COVID-19 pandemic is an emerging respiratory infectious disease, also known as coronavirus 2019. It appears in November 2019 in Hubei province (in China), and more specifically in the city of Wuhan, then spreads in the whole world. As the number of cases increases with unprecedented speed, many parts of the world are facing a shortage of resources and testing. Faced with this problem, physicians, scientists and engineers, including specialists in Artificial Intelligence (AI), have encouraged the development of a Deep Learning model to help healthcare professionals to detect COVID-19 from chest X-ray images and to determine the severity of the infection in a very short time, with low cost. In this paper, we propose CVDNet, a Deep Convolutional Neural Network (CNN) model to classify COVID-19 infection from normal and other pneumonia cases using chest X-ray images. The proposed architecture is based on the residual neural network and it is constructed by using two parallel levels with different kernel sizes to capture local and global features of the inputs. This model is trained on a dataset publically available containing a combination of 219 COVID-19, 1341 normal and 1345 viral pneumonia chest x-ray images. The experimental results reveal that our CVDNet. These results represent a promising classification performance on a small dataset which can be further achieve better results with more training data. Overall, our CVDNet model can be an interesting tool to help radiologists in the diagnosis and early detection of COVID-19 cases.

COVID-19;COVID-19 Pandemic;Communicable Diseases;Infections;Pneumonia;Pneumonia, Viral

Chaos Solitons Fractals

Coronavirus Infections;Health Care;Communicable Diseases

External

2. Detection/Diagnosis

X-Ray

32350794

10.1007/s11547-020-01197-9

Yes

PMC7189175

2020-05-01

Journal Article

Peer reviewed (PubMed)

use of ct and artificial intelligence in suspected or covid-19 positive patients: statement of the italian society of medical and interventional radiology

The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients. SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.

COVID-19;COVID-19 Pandemic;Coronavirus Infections;Pneumonia

Radiol Med

Coronavirus Infections;Polymerase Chain Reaction

N.A.

Review

CT

33134214

10.31661/jbpe.v0i0.2008-1153

Yes

PMC7557468

2020-11-03

Journal Article

Peer reviewed (PubMed)

transfer learning-based automatic detection of coronavirus disease 2019 (covid-19) from chest x-ray images

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease and global health crisis. Although real-time reverse transcription polymerase chain reaction (RT-PCR) is known as the most widely laboratory method to detect the COVID-19 from respiratory specimens. It suffers from several main drawbacks such as time-consuming, high false-negative results, and limited availability. Therefore, the automatically detect of COVID-19 will be required. This study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection of COVID-19 infection in chest X-rays. In a retrospective study, we have applied Visual Geometry Group (VGG)-16, VGG-19, MobileNet, and InceptionResNetV2 pre-trained models for detection COVID-19 infection from 348 chest X-ray images. Our proposed models have been trained and tested on a dataset which previously prepared. The all proposed models provide accuracy greater than 90.0%. The pre-trained MobileNet model provides the highest classification performance of automated COVID-19 classification with 99.1% accuracy in comparison with other three proposed models. The plotted area under curve (AUC) of receiver operating characteristics (ROC) of VGG16, VGG19, MobileNet, and InceptionResNetV2 models are 0.92, 0.91, 0.99, and 0.97, respectively. The all proposed models were able to perform binary classification with the accuracy more than 90.0% for COVID-19 diagnosis. Our data indicated that the MobileNet can be considered as a promising model to detect COVID-19 cases. In the future, by increasing the number of samples of COVID-19 chest X-rays to the training dataset, the accuracy and robustness of our proposed models increase further.

COVID-19;Communicable Diseases, Emerging;Infections

J Biomed Phys Eng

Transfer Learning;Polymerase Chain Reaction;Retrospective Studies;Area under Curve;Communicable Diseases;Reverse Transcription;Receiver Operating Characteristic

External

2. Detection/Diagnosis

X-Ray

2012.01473

Yes

2020-12-02

Preprint

arXiv

covsegnet: a multi encoder-decoder architecture for improved lesion segmentation of covid-19 chest ct scans

Automatic lung lesions segmentation of chest CT scans is considered a pivotal stage towards accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semantic gaps among encoded and decoded feature maps as well as instigate vanishing gradient problems for its sequential gradient propagation that result in sub-optimal performance. Moreover, operating with 3D CT-volume poses further limitations due to the exponential increase of computational complexity making the optimization difficult. In this paper, an automated COVID-19 lesion segmentation scheme is proposed utilizing a highly efficient neural network architecture, namely CovSegNet, to overcome these limitations. Additionally, a two-phase training scheme is introduced where a deeper 2D-network is employed for generating ROI-enhanced CT-volume followed by a shallower 3D-network for further enhancement with more contextual information without increasing computational burden. Along with the traditional vertical expansion of Unet, we have introduced horizontal expansion with multi-stage encoder-decoder modules for achieving optimum performance. Additionally, multi-scale feature maps are integrated into the scale transition process to overcome the loss of contextual information. Moreover, a multi-scale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation. Outstanding performances have been achieved in three publicly available datasets that largely outperform other state-of-the-art approaches. The proposed scheme can be easily extended for achieving optimum segmentation performances in a wide variety of applications.

COVID-19

Art;Architecture;Semantics;Tomography;Map;Cone-Beam Computed Tomography

Self-recorded/clinical

Segmentation-only

CT

33928256

10.1148/ryai.2020200079

Yes

PMC7392327

2021-05-01

Journal Article

Peer reviewed (PubMed)

automated assessment and tracking of covid-19 pulmonary disease severity on chest radiographs using convolutional siamese neural networks

To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on approx. 160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated. PXS scores correlated with radiographic pulmonary disease severity scores assigned to CXRs in the internal and external test sets (r=0.86 (95%CI 0.80-0.90) and r=0.86 (95%CI 0.79-0.90) respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74 (95%CI 0.63-0.81)). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)). A Siamese neural network-based severity score automatically measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict subsequent intubation or death.

COVID-19;Death;Lung Diseases

Radiol Artif Intell

Transfer Learning;Algorithms;Lung;Receiver Operating Characteristic

Self-recorded/clinical

3. Monitoring/Severity assessment

X-Ray

2005.00845

Yes

2020-05-02

Preprint

arXiv

deep convolutional neural networks to diagnose covid-19 and other pneumonia diseases from posteroanterior chest x-rays

The article explores different deep convolutional neural network architectures trained and tested on posteroanterior chest X-rays of 327 patients who are healthy (152 patients), diagnosed with COVID-19 , and other types of pneumonia . In particular, this paper looks at the deep convolutional neural networks VGG16 and VGG19, InceptionResNetV2 and InceptionV3, as well as Xception, all followed by a flat multi-layer perceptron and a final 30% drop-out. The paper has found that the best performing network is VGG16 with a final 30% drop-out trained over 3 classes (COVID-19, No Finding, Other Pneumonia). It has an internal cross-validated accuracy of 93.9%, a COVID-19 sensitivity of 87.7%, and a No Finding sensitivity of 96.8%. The respective external cross-validated values are 84.1%, and 96.8%. The model optimizer was Adam with a 1e-4 learning rate, and categorical cross-entropy loss. It is hoped that, once this research will be put to practice in hospitals, healthcare professionals will be able in the medium to long-term to diagnosing through machine learning tools possible pneumonia, and if detected, whether it is linked to a COVID-19 infection, allowing the detection of new possible COVID-19 foyers after the end of possible "stop-and-go" lockdowns as expected by until a vaccine is found and widespread. Furthermore, in the short-term, it is hoped practitioners can compare the diagnosis from the deep convolutional neural networks with possible RT-PCR testing results, and if clashing, a Computed Tomography could be performed as they are more accurate in showing COVID-19 pneumonia.

COVID-19;Infections;Pneumonia

Health Care;Polymerase Chain Reaction;Neural Networks;Other Topics;Paper

External

2. Detection/Diagnosis

X-Ray

33446781

10.1038/s41598-020-80936-4

Yes

PMC7809065

2021-01-16

Journal Article;Research Support, N.I.H., Extramural;Research Support, Non-U.S. Gov't;Research Support, U.S. Gov't, Non-P.H.S.

Peer reviewed (PubMed)

ct image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of mm and Dice coefficient of . Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

Acute Lung Injury;COVID-19;Lung Cancer;Pulmonary Disease, Chronic Obstructive

Sci Rep

Fibrosis;Neural Networks;Other Topics;Chronic Disease;Cluster Analysis

External

Segmentation-only

CT

33231160

10.2174/1573405616666201123120417

Yes

PMC8653418

2020-11-25

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

deep transfer learning for covid-19 prediction: case study for limited data problems

Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images. This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community. Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation. A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

COVID-19

Curr Med Imaging

Transfer Learning;Neural Networks;Other Topics

External

2. Detection/Diagnosis

X-Ray

2009.08864

Yes

2020-09-15

Preprint

arXiv

classification and region analysis of covid-19 infection using lung ct images and deep convolutional neural networks

COVID-19 is a global health problem. Consequently, early detection and analysis of the infection patterns are crucial for controlling infection spread as well as devising a treatment plan. This work proposes a two-stage deep Convolutional Neural Networks (CNNs) based framework for delineation of COVID-19 infected regions in Lung CT images. In the first stage, initially, COVID-19 specific CT image features are enhanced using a two-level discrete wavelet transformation. These enhanced CT images are then classified using the proposed custom-made deep CoV-CTNet. In the second stage, the CT images classified as infectious images are provided to the segmentation models for the identification and analysis of COVID-19 infectious regions. In this regard, we propose a novel semantic segmentation model CoV-RASeg, which systematically uses average and max pooling operations in the encoder and decoder blocks. This systematic utilization of max and average pooling operations helps the proposed CoV-RASeg in simultaneously learning both the boundaries and region homogeneity. Moreover, the idea of attention is incorporated to deal with mildly infected regions. The proposed two-stage framework is evaluated on a standard Lung CT image dataset, and its performance is compared with the existing deep CNN models. The performance of the proposed CoV-CTNet is evaluated using Mathew Correlation Coefficient (MCC) measure and that of proposed CoV-RASeg using Dice Similarity (DS) score . The promising results on an unseen test set suggest that the proposed framework has the potential to help the radiologists in the identification and analysis of COVID-19 infected regions.

COVID-19;Infections

Health;Semantics

External

2. Detection/Diagnosis

CT

33190102

10.1016/j.ejrad.2020.109402

Yes

PMC7641539

2020-11-16

Journal Article

Peer reviewed (PubMed)

deep learning analysis provides accurate covid-19 diagnosis on chest computed tomography

Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive and low negative likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity and specificity differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates. Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.

COVID-19

Eur J Radiol

Reproducibility of Results;ROC Curve;Lung Diseases;Age

External

2. Detection/Diagnosis

CT

35789224

10.1109/JSEN.2021.3076767

Yes

PMC8791443

2021-04-30

Journal Article

Peer reviewed (PubMed)

blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

COVID-19

IEEE Sens J

Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

CT

33094700

10.1152/physiolgenomics.00084.2020

Yes

PMC7774002

2020-10-24

Journal Article

Peer reviewed (PubMed)

implementation of convolutional neural network approach for covid-19 disease detection

In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 versus normal versus pneumonia) with 98.27% average accuracy. The hyperparameters of both CNN models are automatically determined using Grid Search. Experimental results on large clinical data sets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome the disadvantages mentioned above. Moreover, the proposed CNN models are fully automatic in terms of not requiring the extraction of diseased tissue, which is a great improvement of available automatic methods in the literature. To the best of the author's knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN, whose hyperparameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN-based COVID-19 chest X-ray image classification study that uses the largest possible clinical data set. A total of 1,524 COVID-19, 1,527 pneumonia, and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.

COVID-19;Pneumonia

Physiol Genomics

Architecture;Image Processing;Neural Networks

External

2. Detection/Diagnosis

X-Ray

32730215

10.1109/TMI.2020.3000314

Yes

2020-07-31

Journal Article

Peer reviewed (PubMed)

a noise-robust framework for automatic segmentation of covid-19 pneumonia lesions from ct images

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: our noise-robust Dice loss outperforms existing noise-robust loss functions, the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.

COVID-19;Pneumonia

IEEE Trans Med Imaging

Coronavirus Infections;Art;Algorithms;Noise;Tomography

Self-recorded/clinical

Segmentation-only

CT

33038076

10.2196/21604

Yes

PMC7674140

2020-10-11

Journal Article

Peer reviewed (PubMed)

prediction of covid-19 severity using chest computed tomography and laboratory measurements: evaluation using a machine learning approach

Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

COVID-19;Clinical Course;Infections

JMIR Med Inform

Other Topics

Self-recorded/clinical

4. Prognosis/Treatment

CT

33275187

10.1007/s13246-020-00952-6

Yes

PMC7715648

2020-12-05

Journal Article

Peer reviewed (PubMed)

stacknet-denvis: a multi-layer perceptron stacked ensembling approach for covid-19 detection using x-ray images

The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have suggested that visible irregularities found in the chest X-ray of Covid-19 positive patients are indicative of the presence of the disease. Hence, Deep Learning and Image Classification techniques can be employed to learn from these irregularities, and classify accordingly with high accuracy. This research presents an approach to create a classifier model named StackNet-DenVIS which is designed to act as a screening process before conducting the existing swab tests. Using a novel approach, which incorporates Transfer Learning and Stacked Generalization, the model aims to lower the False Negative rate of classification compensating for the 30% False Negative rate of the swab tests. A dataset gathered from multiple reliable sources consisting of 9953 Chest X-rays (868 Covid and 9085 Non-Covid) was used. Also, this research demonstrates handling data imbalance using various techniques involving Generative Adversarial Networks and sampling techniques. The accuracy, sensitivity, and specificity obtained on our proposed model were 95.07%, 99.40% and 94.61% respectively. To the best of our knowledge, the combination of accuracy and false negative rate obtained by this paper outperforms the current implementations. We must also highlight that our proposed architecture also considers other types of viral pneumonia. Given the unprecedented sensitivity of our model we are optimistic it contributes to a better Covid-19 detection.

COVID-19;Pneumonia, Viral

Phys Eng Sci Med

Transfer Learning;Algorithms;Architecture;COVID-19 Testing;Sensitivity and Specificity;Image Processing;Lung;Neural Networks;Paper;ROC Curve;Lung Diseases;Research

External

2. Detection/Diagnosis

X-Ray

2005.01577

Yes

2020-04-29

Preprint

arXiv

covid-da: deep domain adaptation from typical pneumonia to covid-19

The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to screen chest radiography images, e.g., X-Ray or CT images. However, such examinations are time-consuming and labor-intensive, leading to limited diagnostic efficiency. To solve this issue, AI-based technologies, such as deep learning, have been used recently as effective computer-aided means to improve diagnostic efficiency. However, one practical and critical difficulty is the limited availability of annotated COVID-19 data, due to the prohibitive annotation costs and urgent work of doctors to fight against the pandemic. This makes the learning of deep diagnosis models very challenging. To address this, motivated by that typical pneumonia has similar characteristics with COVID-19 and many pneumonia datasets are publicly available, we propose to conduct domain knowledge adaptation from typical pneumonia to COVID-19. There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19. To address them, we propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA. Specifically, we alleviate the domain discrepancy via feature adversarial adaptation and handle the task difference issue via a novel classifier separation scheme. In this way, COVID-DA is able to diagnose COVID-19 effectively with only a small number of COVID-19 annotations. Extensive experiments verify the effectiveness of COVID-DA and its great potential for real-world applications.

COVID-19;Infections;Pneumonia

Disease Outbreaks;Other Topics

External

2. Detection/Diagnosis

X-Ray

33132536

10.1007/s00521-020-05437-x

Yes

PMC7586204

2020-11-03

Journal Article

Peer reviewed (PubMed)

a deep transfer learning model with classical data augmentation and cgan to detect covid-19 from chest ct radiography digital images

The Coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with Conditional Generative Adversarial Nets (CGAN) based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for COVID-19 especially in chest CT images are the main motivation of this research. The main idea is to collect all the possible images for COVID-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the Coronavirus-infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%, sensitivity 77.66%, and specificity of 87.62%.

COVID-19;Severe Acute Respiratory Syndrome

Neural Comput Appl

Health Care;Transfer Learning;Sensitivity and Specificity;Health Care Systems

External

2. Detection/Diagnosis

CT

32722697

10.1371/journal.pone.0236621

Yes

PMC7386587

2020-07-30

Journal Article

Peer reviewed (PubMed)

deep transfer learning artificial intelligence accurately stages covid-19 lung disease severity on portable chest radiographs

This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1yo; 29F 60.1yo; 4 missing information). Three expert chest radiologists scored the left and right lung separately based on the degree of opacity and geographic extent . Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. Data were split into 80% training and 20% testing datasets. Correlation analysis between AI-predicted versus radiologist scores were analyzed. Comparison was made with traditional and transfer learning. The average opacity score was 2.52 (range: 0-6) with a standard deviation of 0.25 across three readers. The average geographic extent score was 3.42 (range: 0-8) with a standard deviation of 0.57 across three readers. The inter-rater agreement yielded a Fleiss' Kappa of 0.45 for opacity score and 0.71 for extent score. AI-predicted scores strongly correlated with radiologist scores, with the top model yielding a correlation coefficient (R2) of 0.90 (range: 0.73-0.90 for traditional learning and 0.83-0.90 for transfer learning) and a mean absolute error of 8.5% (ranges: 17.2-21.0% and 8.5%-15.5, respectively). Transfer learning generally performed better. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This approach may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.

COVID-19;Infections;Lung Diseases

PLoS One

Other Topics

External

3. Monitoring/Severity assessment

X-Ray

10.1101/2020.10.30.20222786

10.1101/2020.10.30.20222786

Yes

2020-11-03

Preprint

medRxiv

deep learning model for improving the characterization of coronavirus on chest x-ray images using cnn

The novel Coronavirus, also known as Covid19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Although researches into the production of relevant vaccine are being advanced, there is, however, a need for a computational solution to mediate the process of aiding quick detection of the disease. Different computational solutions comprised of natural language processing, knowledge engineering and deep learning have been adopted for this task. However, deep learning solutions have shown interesting performance compared to other methods. This paper therefore aims to advance the application deep learning technique to the problem of characterization and detection of novel coronavirus. The approach adopted in this study proposes a convolutional neural network (CNN) model which is further enhanced using the technique of data augmentation. The motive for the enhancement of the CNN model through the latter technique is to investigate the possibility of further improving the performances of deep learning models in detection of coronavirus. The proposed model is then applied to the COVID-19 X-ray dataset in this study which is the National Institutes of Health (NIH) Chest X-Ray dataset obtained from Kaggle for the purpose of promoting early detection and screening of coronavirus disease. Results obtained showed that our approach achieved a performance of 100% accuracy, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. The proposed CNN model and data augmentation solution may be adopted in pre-screening suspected cases of Covid19 to provide support to the use of the well-known RT-PCR testing.

COVID-19

Polymerase Chain Reaction;Other Topics

External

2. Detection/Diagnosis

X-Ray

2010.14091

Yes

2020-10-27

Preprint

arXiv

triple-view convolutional neural networks for covid-19 diagnosis with chest x-ray

The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large number of people worldwide, posing significant stress to the health care systems. Early and accurate diagnosis of COVID-19 is critical in screening of infected patients and breaking the person-to-person transmission. Chest X-ray (CXR) based computer-aided diagnosis of COVID-19 using deep learning becomes a promising solution to this end. However, the diverse and various radiographic features of COVID-19 make it challenging, especially when considering each CXR scan typically only generates one single image. Data scarcity is another issue since collecting large-scale medical CXR data set could be difficult at present. Therefore, how to extract more informative and relevant features from the limited samples available becomes essential. To address these issues, unlike traditional methods processing each CXR image from a single view, this paper proposes triple-view convolutional neural networks for COVID-19 diagnosis with CXR images. Specifically, the proposed networks extract individual features from three views of each CXR image, i.e., the left lung view, the right lung view and the overall view, in three streams and then integrate them for joint diagnosis. The proposed network structure respects the anatomical structure of human lungs and is well aligned with clinical diagnosis of COVID-19 in practice. In addition, the labeling of the views does not require experts' domain knowledge, which is needed by many existing methods. The experimental results show that the proposed method achieves state-of-the-art performance, especially in the more challenging three class classification task, and admits wide generality and high flexibility.

COVID-19

Art;Health Care;Other Topics

External

2. Detection/Diagnosis

X-Ray

33230398

10.1016/j.bspc.2020.102365

Yes

PMC7674150

2020-11-25

Journal Article

Peer reviewed (PubMed)

application of deep learning techniques for detection of covid-19 cases using chest x-ray images: a comprehensive study

The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.

COVID-19;Infections

Biomed Signal Process Control

Research Personnel;Polymerase Chain Reaction;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

32729263

10.3348/kjr.2020.0536

Yes

PMC7458860

2020-07-31

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

implementation of a deep learning-based computer-aided detection system for the interpretation of chest radiographs in patients suspected for covid-19

To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

COVID-19;Pneumonia

Korean J Radiol

Coronavirus Infections;COVID-19 Testing;Polymerase Chain Reaction;Retrospective Studies

Self-recorded/clinical

2. Detection/Diagnosis

Multimodal

2009.12597

Yes

2021-01-21

Preprint

arXiv

potential features of icu admission in x-ray images of covid-19 patients

X-ray images may present non-trivial features with predictive information of patients that develop severe symptoms of COVID-19. If true, this hypothesis may have practical value in allocating resources to particular patients while using a relatively inexpensive imaging technique. The difficulty of testing such a hypothesis comes from the need for large sets of labelled data, which need to be well-annotated and should contemplate the post-imaging severity outcome. This paper presents an original methodology for extracting semantic features that correlate to severity from a data set with patient ICU admission labels through interpretable models. The methodology employs a neural network trained to recognise lung pathologies to extract the semantic features, which are then analysed with low-complexity models to limit overfitting while increasing interpretability. This analysis points out that only a few features explain most of the variance between patients that developed severe symptoms. When applied to an unrelated larger data set with pathology-related clinical notes, the method has shown to be capable of selecting images for the learned features, which could translate some information about their common locations in the lung. Besides attesting separability on patients that eventually develop severe symptoms, the proposed methods represent a statistical approach highlighting the importance of features related to ICU admission that may have been only qualitatively reported. While handling limited data sets, notable methodological aspects are adopted, such as presenting a state-of-the-art lung segmentation network and the use of low-complexity models to avoid overfitting. The code for methodology and experiments is also available.

COVID-19

Art;Semantics;Other Topics

External

4. Prognosis/Treatment

X-Ray

34764554

10.1007/s10489-020-01900-3

Yes

PMC7568031

2021-11-13

Journal Article

Peer reviewed (PubMed)

automated diagnosis of covid-19 with limited posteroanterior chest x-ray images using fine-tuned deep neural networks

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.

COVID-19;Infections;Pneumonia;Syndrome

Appl Intell (Dordr)

Art;Health Care;Transfer Learning;Research Personnel;Architecture;Polymerase Chain Reaction;Tomography;Area under Curve

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.05.24.20111922

10.1101/2020.05.24.20111922

Yes

2020-05-25

Preprint

medRxiv

aidcov: an interpretable artificial intelligence model for detection of covid-19 from chest radiography images

As the Coronavirus Disease 2019 (COVID-19) pandemic continues to grow globally, testing to detect COVID-19 and isolating individuals who test positive remains to be the primary strategy for preventing community spread of the disease. The current gold standard method of testing for COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) test. The RT-PCR test, however, has an imperfect sensitivity (around 70%), is time-consuming and labor-intensive, and is in short supply, particularly in resource-limited countries. Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities such as chest X-ray and Computed Tomography, which are more widely available and accessible, can be beneficial. We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). We refer to this model as Artificial Intelligence for Detection of COVID-19 (AIDCOV). The hierarchical structure in AIDCOV captures the dependency of features and improves model performance while the attention mechanism makes the model interpretable and transparent. Using a publicly available dataset of 5801 chest images, we demonstrate that our model achieves a mean cross-validation accuracy of 97.8%. AIDCOV has a sensitivity of 99.3%, a specificity of 99.98%, and a positive predictive value of 99.6% in detecting COVID-19 from chest radiography images. AIDCOV can be used in conjunction with or instead of RT-PCR testing (where RT-PCR testing is unavailable) to detect and isolate individuals with COVID-19 and prevent onward transmission to the general population and healthcare workers.

COVID-19;COVID-19 Pandemic;Infections;Pneumonia

Predictive Value;Sensitivity and Specificity;Polymerase Chain Reaction;Neural Networks;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

32836918

10.1016/j.chaos.2020.110190

Yes

PMC7413068

2020-08-25

Journal Article

Peer reviewed (PubMed)

a deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in ≤ 2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

COVID-19;COVID-19 Pandemic;Infections;Pneumonia

Chaos Solitons Fractals

Algorithms;Transfer Learning;Color;Polymerase Chain Reaction

External

2. Detection/Diagnosis

Multimodal

10.1101/2020.05.12.20098954

10.1101/2020.05.12.20098954

Yes

2020-05-19

Preprint

medRxiv

covid-19 detection using cnn transfer learning from x-ray images

The Covid-19 first occurs in Wuhan, China in December 2019. After that the virus spread all around the world and at the time of writing this paper the total number of confirmed cases are above 4.7 million with over 315000 deaths. Machine learning algorithms built on radiography images can be used as decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is twofold. First, a quantitative analysis to evaluate 12 off-the-shelf convolutional neural networks (CNNs) for the purpose of COVID-19 X-ray image analysis. Specifically, CNN transfer learning procedure was adopted due to the small number of images available for investigation. We also proposed a simple CNN architecture with a small number of parameters that perform well on distinguishing COVID-19 from normal X-rays. Secondly, a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed most to the decision of CNNs back to the original image to visualize the most discriminating regions on the input image. Chest X-ray images used in this work are coming from 3 publicly available sources. Two COVID-19 X-ray image datasets and a large dataset of other non-COVID-19 viral infections, bacterial infections and normal X-rays utilised. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect the regions of the input image used by CNNs that lead to its prediction.

Bacterial Infections;COVID-19;Death;Virus Diseases

Algorithms;Transfer Learning;Architecture;Map

External

Segmentation-only

X-Ray

2004.10987

Yes

2020-04-25

Preprint

arXiv

covid-19 chest ct image segmentation -- a deep convolutional neural network solution

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We firstly maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively.

COVID-19;Infections

Polymerase Chain Reaction;Other Topics

Self-recorded/clinical

Segmentation-only

CT

32834627

10.1016/j.chaos.2020.110071

Yes

PMC7332960

2020-08-25

Journal Article

Peer reviewed (PubMed)

recognition of covid-19 disease from x-ray images by hybrid model consisting of 2d curvelet transform chaotic salp swarm algorithm and deep learning technique

The novel coronavirus disease 2019 (COVID-19), detected in Wuhan City, Hubei Province, China in late December 2019, is rapidly spreading and affecting all countries in the world. Real-time reverse transcription-polymerase chain reaction (RT-PCR) test has been described by the World Health Organization (WHO) as the standard test method for the diagnosis of the disease. However, considering that the results of this test are obtained between a few hours and two days, it is very important to apply another diagnostic method as an alternative to this test. The fact that RT-PCR test kits are limited in number, the test results are obtained in a long time, and the high probability of healthcare personnel becoming infected with the disease during the test, necessitates the use of other diagnostic methods as an alternative to these test kits. In this study, a hybrid model consisting of two-dimensional (2D) curvelet transformation, chaotic salp swarm algorithm (CSSA) and deep learning technique is developed in order to determine the patient infected with coronavirus pneumonia from X-ray images. In the proposed model, 2D Curvelet transformation is applied to the images obtained from the patient's chest X-ray radiographs and a feature matrix is formed using the obtained coefficients. The coefficients in the feature matrix are optimized with the help of the CSSA and COVID-19 disease is diagnosed by the EfficientNet-B0 model, which is one of the deep learning methods. Experimental results show that the proposed hybrid model can diagnose COVID-19 disease with high accuracy from chest X-ray images.

COVID-19;Pneumonia

Chaos Solitons Fractals

Occupational Groups;Health Care;World Health Organization;Polymerase Chain Reaction;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

32927416

10.1016/j.ejrad.2020.109233

Yes

PMC7455238

2020-09-15

Journal Article

Peer reviewed (PubMed)

development and clinical implementation of tailored image analysis tools for covid-19 in the midst of the pandemic: the synergetic effect of an open clinically embedded software development platform and machine learning

During the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes how our clinic implemented an automated software solution for this purpose into an established software platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing clinical needs in the wake of a pandemic. Deep convolutional neural network algorithms for lung segmentation and opacity quantification on chest CTs were trained using semi-automatically and manually created ground-truth (Ntotal = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset (N = 66). The final algorithm was available at day 10 and achieved human-like performance (Dice coefficient = 0.97). For opacity quantification, a slight underestimation was seen both for the in-house and for the external algorithm . In contrast to the external reference, the underestimation for the in-house algorithm showed no dependency on total opacity load, making it more suitable for follow-up. The combination of machine learning and a clinically embedded software development platform enabled time-efficient development, instant deployment, and rapid adoption in clinical routine. The algorithm for fully automated lung segmentation and opacity quantification that we developed in the midst of the COVID-19 pandemic was ready for clinical use within just 10 days and achieved human-level performance even in complex cases.

COVID-19;COVID-19 Pandemic

Eur J Radiol

Coronavirus Infections;Neural Networks

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

10.1101/2020.12.19.20248530

10.1101/2020.12.19.20248530

Yes

2020-12-23

Preprint

medRxiv

lungai: a deep learning convolutional neural network for automated detection of covid-19 from posteroanterior chest x-rays

COVID-19 is an infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). As of December 2020, more than 72 million cases have been reported worldwide. The standard method of diagnosis is by Real-Time Reverse Transcription Polymerase Chain Reaction (rRT-PCR) from a Nasopharyngeal Swab. Currently, there is no vaccine or specific antiviral treatment for COVID-19. Due to rate of spreading of the disease manual detection among people is becoming more difficult because of a clear lack of testing capability. Thus there was need of a quick and reliable yet non-labour intensive detection technique. Considering that the virus predominantly appears in the form of a lung based abnormality I made use of Chest X-Rays as our primary mode of detection. For this detection system we made use of Posteroanterior (PA) Chest X-rays of people infected with Bacterial Pneumonia (2780 Images), Viral Pneumonia (1493 Images), Covid-19 (729 Images) as well as those of perfectly Healthy Individuals (1583 Images) procured from various Publicly Available Datasets and Radiological Societies. LungAI is a novel Convolutional Neural Network based on a Hybrid of the DarkNet and AlexNet architecture. The network was trained on 80% of the dataset with 20% kept for validation. The proposed Coronavirus Detection Model performed exceedingly well with an accuracy of 99.16%, along with a Sensitivity value of 99.31% and Specificity value of 99.14%. Thus LungAI has the potential to prove useful in managing the current Pandemic Situation by providing a reliable and fast alternative to Coronavirus Detection given strong results.

COVID-19;Communicable Diseases;Pneumonia, Bacterial;Pneumonia, Viral;Severe Acute Respiratory Syndrome

Polymerase Chain Reaction;Antiviral Agents;Pharmaceutical Preparations;Communicable Diseases;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

32568676

10.1016/j.compbiomed.2020.103795

Yes

PMC7190523

2020-06-23

Journal Article

Peer reviewed (PubMed)

application of deep learning technique to manage covid-19 in routine clinical practice using ct images: results of 10 convolutional neural networks

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.

COVID-19;Infections;Pneumonia, Viral

Comput Biol Med

Coronavirus Infections;Sensitivity and Specificity;Neural Networks;Area under Curve

External

2. Detection/Diagnosis

CT

33230503

10.1016/j.ibmed.2020.100014

Yes

PMC7674009

2020-11-25

Journal Article

Peer reviewed (PubMed)

covid-19 pneumonia accurately detected on chest radiographs with artificial intelligence

To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007). Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.

COVID-19;Pneumonia;Pneumonia, Bacterial

Intell Based Med

Polymerase Chain Reaction;Real-Time Polymerase Chain Reaction

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

33209367

10.21037/jtd-20-1584

Yes

PMC7656439

2020-11-20

Journal Article

Peer reviewed (PubMed)

ct imaging features of different clinical types of covid-19 calculated by ai system: a chinese multicenter study

The study is designed to explore the chest CT features of different clinical types of coronavirus disease 2019 (COVID-19) pneumonia based on a Chinese multicenter dataset using an artificial intelligence (AI) system. A total of 164 patients confirmed COVID-19 were retrospectively enrolled from 6 hospitals. All patients were divided into the mild type (136 cases) and the severe type (28 cases) according to their clinical manifestations. The total CT severity score and quantitative CT features were calculated by AI pneumonia detection and evaluation system with correction by radiologists. The clinical and CT imaging features of different types were analyzed. It was observed that patients in the severe type group were older than the mild type group. Round lesions, Fan-shaped lesions, crazy-paving pattern, fibrosis, "white lung", pleural thickening, pleural indentation, mediastinal lymphadenectasis were more common in the CT images of severe patients than in the mild ones. A higher total lung severity score and scores of each lobe were observed in the severe group, with higher scores in bilateral lower lobes of both groups. Further analysis showed that the volume and number of pneumonia lesions and consolidation lesions in overall lung were higher in the severe group, and showed a wider distribution in the lower lobes of bilateral lung in both groups. Chest CT of patients with severe COVID-19 pneumonia showed more consolidative and progressive lesions. With the assistance of AI, CT could evaluate the clinical severity of COVID-19 pneumonia more precisely and help the early diagnosis and surveillance of the patients.

COVID-19;Fibrosis;Pneumonia

J Thorac Dis

Dataset;Fibrosis;Radiologists

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

2003.13145

10.1109/ACCESS.2020.3010287

Yes

2020-06-15

Preprint

arXiv

can ai help in screening viral and covid-19 pneumonia?

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.

COVID-19;Infections;Pneumonia;Pneumonia, Viral

Coronavirus Infections;Occupational Groups;Health Care;Algorithms;Transfer Learning;Sensitivity and Specificity;Polymerase Chain Reaction;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

2004.09803

Yes

2020-04-21

Preprint

arXiv

covidaid: covid-19 detection using chest x-ray

The exponential increase in COVID-19 patients is overwhelming healthcare systems across the world. With limited testing kits, it is impossible for every patient with respiratory illness to be tested using conventional techniques (RT-PCR). The tests also have long turn-around time, and limited sensitivity. Detecting possible COVID-19 infections on Chest X-Ray may help quarantine high risk patients while test results are awaited. X-Ray machines are already available in most healthcare systems, and with most modern X-Ray systems already digitized, there is no transportation time involved for the samples either. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. This may be useful in an inpatient setting where the present systems are struggling to decide whether to keep the patient in the ward along with other patients or isolate them in COVID-19 areas. It would also help in identifying patients with high likelihood of COVID with a false negative RT-PCR who would need repeat testing. Further, we propose the use of modern AI techniques to detect the COVID-19 patients using X-Ray images in an automated manner, particularly in settings where radiologists are not available, and help make the proposed testing technology scalable. We present CovidAID: COVID-19 AI Detector, a novel deep neural network based model to triage patients for appropriate testing. On the publicly available covid-chestxray-dataset , our model gives 90.5% accuracy with 100% sensitivity (recall) for the COVID-19 infection. We significantly improve upon the results of Covid-Net on the same dataset.

COVID-19;Infections

Health Care;Polymerase Chain Reaction;Other Topics

External

2. Detection/Diagnosis

X-Ray

33091743

10.1016/j.media.2020.101844

Yes

PMC7553063

2020-10-23

Journal Article;Research Support, N.I.H., Extramural

Peer reviewed (PubMed)

integrative analysis for covid-19 patient outcome prediction

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at GitHub

COVID-19;Disease Progression;Lung Diseases;Pneumonia

Med Image Anal

Predictive Value;COVID-19 Testing;Polymerase Chain Reaction;Area under Curve;Reverse Transcription

Self-recorded/clinical

4. Prognosis/Treatment

CT

33821166

10.1016/j.bspc.2021.102588

Yes

PMC8011666

2021-04-07

Journal Article

Peer reviewed (PubMed)

a fully automated deep learning-based network for detecting covid-19 from a new and large lung ct scan dataset

This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at GitHub

COVID-19;Infections

Biomed Signal Process Control

Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

CT

33328512

10.1038/s41598-020-79097-1

Yes

PMC7745019

2020-12-18

Journal Article;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

prediction of disease progression in patients with covid-19 by artificial intelligence assisted lesion quantification

To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 cm3 versus 101.12 cm3, p = 0.013) as well as consolidation volume (40.85 cm3 versus 6.63 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.

COVID-19;Disease Progression;Infections

Sci Rep

Severity of Illness Index;ROC Curve;Retrospective Studies;Lung Diseases;Age

Self-recorded/clinical

4. Prognosis/Treatment

CT

2012.05073

Yes

2020-12-17

Preprint

arXiv

covid-19 detection in chest x-ray images using a new channel boosted cnn

COVID-19 is a highly contagious respiratory infection that has affected a large population across the world and continues with its devastating consequences. It is imperative to detect COVID-19 at the earliest to limit the span of infection. In this work, a new classification technique CB-STM-RENet based on deep Convolutional Neural Network (CNN) and Channel Boosting is proposed for the screening of COVID-19 in chest X-Rays. In this connection, to learn the COVID-19 specific radiographic patterns, a new convolution block based on split-transform-merge (STM) is developed. This new block systematically incorporates region and edge-based operations at each branch to capture the diverse set of features at various levels, especially those related to region homogeneity, textural variations, and boundaries of the infected region. The learning and discrimination capability of the proposed CNN architecture is enhanced by exploiting the Channel Boosting idea that concatenates the auxiliary channels along with the original channels. The auxiliary channels are generated from the pre-trained CNNs using Transfer Learning. The effectiveness of the proposed technique CB-STM-RENet is evaluated on three different datasets of chest X-Rays namely CoV-Healthy-6k, CoV-NonCoV-10k, and CoV-NonCoV-15k. The performance comparison of the proposed CB-STM-RENet with the existing techniques exhibits high performance both in discriminating COVID-19 chest infections from Healthy, as well as, other types of chest infections. CB-STM-RENet provides the highest performance on all these three datasets; especially on the stringent CoV-NonCoV-15k dataset. The good detection rate , and high precision of the proposed technique suggest that it can be adapted for the diagnosis of COVID-19 infected patients.GitHub

COVID-19;Infections;Respiratory Tract Infections

Transfer Learning;Architecture

External

2. Detection/Diagnosis

X-Ray

33164982

10.3233/XST-200735

Yes

PMC7990455

2020-11-10

Journal Article;Research Support, N.I.H., Intramural;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

using artificial intelligence to assist radiologists in distinguishing covid-19 from other pulmonary infections

Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans. For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infection cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance. Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists' performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance. A deep learning algorithm-based AI model developed in this study successfully improved radiologists' performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

COVID-19;Infections;Pneumonia;Pneumonia, Viral;Tuberculosis

J Xray Sci Technol

Coronavirus Infections;Algorithms;ROC Curve;Lung Diseases;Age

Self-recorded/clinical

2. Detection/Diagnosis

CT

32837679

10.1016/j.irbm.2020.07.001

Yes

PMC7333623

2020-08-25

Journal Article

Peer reviewed (PubMed)

automated deep transfer learning-based approach for detection of covid-19 infection in chest x-rays

The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models.

COVID-19;Infections

Ing Rech Biomed

Coronavirus Infections;Transfer Learning;Polymerase Chain Reaction;Tomography;Real-Time Polymerase Chain Reaction

External

2. Detection/Diagnosis

X-Ray

32804113

10.3233/XST-200720

Yes

PMC7592683

2020-08-18

Comparative Study;Journal Article

Peer reviewed (PubMed)

detection of coronavirus disease from x-ray images using deep learning and transfer learning algorithms

This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. This study applied transfer learning method to develop deep learning models for detecting COVID-19 disease. Three existing state-of-the-art deep learning models namely, Inception ResNetV2, InceptionNetV3 and NASNetLarge, were selected and fine-tuned to automatically detect and diagnose COVID-19 disease using chest X-ray images. A dataset involving 850 images with the confirmed COVID-19 disease, 500 images of community-acquired (non-COVID-19) pneumonia cases and 915 normal chest X-ray images was used in this study. Among the three models, InceptionNetV3 yielded the best performance with accuracy levels of 98.63% and 99.02% with and without using data augmentation in model training, respectively. All the performed networks tend to overfitting (with high training accuracy) when data augmentation is not used, this is due to the limited amount of image data used for training and validation. This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.

COVID-19;Pneumonia

J Xray Sci Technol

Coronavirus Infections;Art;Transfer Learning;Algorithms;Lung;Neural Networks;Tomography;Lung Diseases;Early Diagnosis

External

2. Detection/Diagnosis

X-Ray

33162872

10.1016/j.asoc.2020.106859

Yes

PMC7598372

2020-11-10

Journal Article

Peer reviewed (PubMed)

instacovnet-19: a deep learning classification model for the detection of covid-19 patients using chest x-ray

Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19's ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

COVID-19;Pneumonia

Appl Soft Comput

Other Topics

External

2. Detection/Diagnosis

X-Ray

32427924

10.1038/s41591-020-0931-3

Yes

PMC7446729

2020-05-20

Journal Article;Research Support, N.I.H., Extramural;Research Support, Non-U.S. Gov't

Peer reviewed (PubMed)

artificial intelligence-enabled rapid diagnosis of patients with covid-19

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

COVID-19

Nat Med

Coronavirus Infections;Predictive Value;Polymerase Chain Reaction;Real-Time Polymerase Chain Reaction

Self-recorded/clinical

2. Detection/Diagnosis

CT

33171999

10.3390/jcm9113576

Yes

PMC7694629

2020-11-12

Journal Article

Peer reviewed (PubMed)

accuracy of conventional and machine learning enhanced chest radiography for the assessment of covid-19 pneumonia: intra-individual comparison with ct

To evaluate diagnostic accuracy of conventional radiography (CXR) and machine learning enhanced CXR (mlCXR) for the detection and quantification of disease-extent in COVID-19 patients compared to chest-CT. Real-time polymerase chain reaction (rt-PCR)-confirmed COVID-19-patients undergoing CXR from March to April 2020 together with COVID-19 negative patients as control group were retrospectively included. Two independent readers assessed CXR and mlCXR images for presence, disease extent and type (consolidation vs. ground-glass opacities (GGOs) of COVID-19-pneumonia. Further, readers had to assign confidence levels to their diagnosis. CT obtained ≤ 36 h from acquisition of CXR served as standard of reference. Inter-reader agreement, sensitivity for detection and disease extent of COVID-19-pneumonia compared to CT was calculated. McNemar test was used to test for significant differences. Sixty patients (21 females; median age 61 years, range 38-81 years) were included. Inter-reader agreement improved from good to excellent when mlCXR instead of CXR was used (k = 0.831 vs. k = 0.742). Sensitivity for pneumonia detection improved from 79.5% to 92.3%, however, on the cost of specificity 100% vs. 71.4% (p = 0.031). Overall, sensitivity for the detection of consolidation was higher than for GGO (37.5% vs. 70.4%; respectively). No differences could be found in disease extent estimation between mlCXR and CXR, even though the detection of GGO could be improved. Diagnostic confidence was better on mlCXR compared to CXR (p = 0.013). In line with the current literature, the sensitivity for detection and quantification of COVID-19-pneumonia was moderate with CXR and could be improved when mlCXR was used for image interpretation.

COVID-19;Pneumonia

J Clin Med

Polymerase Chain Reaction;Real-Time Polymerase Chain Reaction

Self-recorded/clinical

3. Monitoring/Severity assessment

Multimodal

2006.13873

Yes

2020-06-18

Preprint

arXiv

covidlite: a depth-wise separable deep neural network with white balance and clahe for detection of covid-19

Currently, the whole world is facing a pandemic disease, novel Coronavirus also known as COVID-19, which spread in more than 200 countries with around 3.3 million active cases and 4.4 lakh deaths approximately. Due to rapid increase in number of cases and limited supply of testing kits, availability of alternative diagnostic method is necessary for containing the spread of COVID-19 cases at an early stage and reducing the death count. For making available an alternative diagnostic method, we proposed a deep neural network based diagnostic method which can be easily integrated with mobile devices for detection of COVID-19 and viral pneumonia using Chest X-rays (CXR) images. In this study, we have proposed a method named COVIDLite, which is a combination of white balance followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and depth-wise separable convolutional neural network (DSCNN). In this method, white balance followed by CLAHE is used as an image preprocessing step for enhancing the visibility of CXR images and DSCNN trained using sparse cross entropy is used for image classification with lesser parameters and significantly lighter in size, i.e., 8.4 MB without quantization. The proposed COVIDLite method resulted in improved performance in comparison to vanilla DSCNN with no pre-processing. The proposed method achieved higher accuracy of 99.58% for binary classification, whereas 96.43% for multiclass classification and out-performed various state-of-the-art methods. Our proposed method, COVIDLite achieved exceptional results on various performance metrics. With detailed model interpretations, COVIDLite can assist radiologists in detecting COVID-19 patients from CXR images and can reduce the diagnosis time significantly.

COVID-19;Death;Pneumonia, Viral

Art;Radiologists;Other Topics;Entropy

External

2. Detection/Diagnosis

X-Ray

2004.12592

Yes

2020-05-21

Preprint

arXiv

robust screening of covid-19 from chest x-ray via discriminative cost-sensitive learning

This paper addresses the new problem of automated screening of coronavirus disease 2019 (COVID-19) based on chest X-rays, which is urgently demanded toward fast stopping the pandemic. However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive. While a few pioneering works have made much progress, they underestimate both crucial bottlenecks. In this paper, we report our solution, discriminative cost-sensitive learning (DCSL), which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays. DCSL combines both advantages from fine-grained classification and cost-sensitive learning. Firstly, DCSL develops a conditional center loss that learns deep discriminative representation. Secondly, DCSL establishes score-level cost-sensitive learning that can adaptively enlarge the cost of misclassifying COVID-19 examples into other classes. DCSL is so flexible that it can apply in any deep neural network. We collected a large-scale multi-class dataset comprised of 2,239 chest X-ray examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy people. Extensive experiments on the three-class classification show that our algorithm remarkably outperforms state-of-the-art algorithms. It achieves an accuracy of 97.01%, a precision of 97%, a sensitivity of 97.09%, and an F1-score of 96.98%. These results endow our algorithm as an efficient tool for the fast large-scale screening of COVID-19.

COVID-19;Pneumonia;Pneumonia, Viral

Art;Algorithms

External

2. Detection/Diagnosis

X-Ray

33250662

10.1007/s00500-020-05424-3

Yes

PMC7679792

2020-12-01

Journal Article

Peer reviewed (PubMed)

covid-chexnet: hybrid deep learning framework for identifying covid-19 virus in chest x-rays images

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

COVID-19

Soft comput

Radiography;Health Care;Transfer Learning;Architecture;Disease Outbreaks;Noise;Sensitivity and Specificity;Radiologists

External

2. Detection/Diagnosis

X-Ray

32969949

10.1097/RTI.0000000000000559

Yes

2020-09-25

Journal Article

Peer reviewed (PubMed)

detection of covid-19 using deep learning algorithms on chest radiographs

To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR). In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC). The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test). A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.

COVID-19;Fever;Pneumonia

J Thorac Imaging

Health Care;Sensitivity and Specificity;Polymerase Chain Reaction;Radiologists;Retrospective Studies;Area under Curve;Receiver Operating Characteristic

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.05.11.20097907

10.1101/2020.05.11.20097907

Yes

2020-05-29

Preprint

medRxiv

online covid-19 diagnosis with chest ct images: lesion-attention deep neural networks

Chest computed tomography (CT) scanning is one of the most important technologies for COVID-19 diagnosis and disease monitoring, particularly for early detection of coronavirus. Recent advancements in computer vision motivate more concerted efforts in developing AI-driven diagnostic tools to accommodate the enormous demands for the COVID-19 diagnostic tests globally. To help alleviate burdens on medical systems, we develop a lesion-attention deep neural network (LA-DNN) to predict COVID-19 positive or negative with a richly annotated chest CT image dataset. Based on the textual radiological report accompanied with each CT image, we extract two types of important information for the annotations: One is the indicator of a positive or negative case of COVID-19, and the other is the description of five lesions on the CT images associated with the positive cases. The proposed data-efficient LA-DNN model focuses on the primary task of binary classification for COVID-19 diagnosis, while an auxiliary multi-label learning task is implemented simultaneously to draw the model’s attention to the five lesions associated with COVID-19. The joint task learning process makes it a highly sample-efficient deep neural network that can learn COVID-19 radiology features more effectively with limited but high-quality, rich-information samples. The experimental results show that the area under the curve (AUC) and sensitivity (recall), precision, and accuracy for COVID-19 diagnosis are 94.0%, 88.8%, 87.9%, and 88.6% respectively, which reach the clinical standards for practical use. A free online system is currently alive for fast diagnosis using CT images at the website /, and all codes and datasets are freely accessible at our github address.

COVID-19