Datasets:

zurlog

/

subset_wlabels

like 0

32804639

10.1109/MPULS.2020.3008354

Yes

2020-08-18

Journal Article

Peer reviewed (PubMed)

ai-driven covid-19 tools to interpret quantify lung images

Qualitative interpretation is a good thing when it comes to reading lung images in the fight against coronavirus 2019 disease (COVID-19), but quantitative analysis makes radiology reporting much more comprehensive. To that end, several research groups have begun looking to artificial intelligence (AI) as a tool for reading and analyzing X-rays and computed tomography (CT) scans, and helping to diagnose and monitor COVID-19.

COVID-19

IEEE Pulse

Other Topics

N.A.

Review

Multimodal

10.1101/2020.08.20.20178723

10.1101/2020.08.20.20178723

Yes

2020-08-23

Preprint

medRxiv

automatic analysis system of covid-19 radiographic lung images (xraycovidetector)

COVID-19 is a pandemic infectious disease caused by the SARS-CoV-2 virus, having reached more than 210 countries and territories. It produces symptoms such as fever, dry cough, dyspnea, fatigue, pneumonia, and radiological manifestations. The most common reported RX and CT findings include lung consolidation and ground-glass opacities. In this paper, we describe a machine learning-based system (XrayCoviDetector; until the image has a size ), that detects automatically, the probability that a thorax radiological image includes COVID-19 lung patterns. XrayCoviDetector has an accuracy of 0.93, a sensitivity of 0.96, and a specificity of 0.90.

COVID-19;Communicable Diseases;Cough;Dyspnea;Fatigue;Fever;Pneumonia

Specificity;Communicable Diseases;Eyeglasses

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

32703538

10.1067/j.cpradiol.2020.06.009

Yes

PMC7320858

2020-07-25

Journal Article;Review

Peer reviewed (PubMed)

current landscape of imaging and the potential role for artificial intelligence in the management of covid-19

The clinical management of COVID-19 is challenging. Medical imaging plays a critical role in the early detection, clinical monitoring and outcomes assessment of this disease. Chest x-ray radiography and computed tomography) are the standard imaging modalities used for the structural assessment of the disease status, while functional imaging (namely, positron emission tomography) has had limited application. Artificial intelligence can enhance the predictive power and utilization of these imaging approaches and new approaches focusing on detection, stratification and prognostication are showing encouraging results. We review the current landscape of these imaging modalities and artificial intelligence approaches as applied in COVID-19 management.

COVID-19

Curr Probl Diagn Radiol

Health Care;Other Topics

N.A.

Review

Multimodal

10.1101/2020.04.21.20072637

10.1101/2020.04.21.20072637

Yes

2020-04-25

Preprint

medRxiv

research on cnn-based models optimized by genetic algorithm and application in the diagnosis of pneumonia and covid-19

In this research, an optimized deep learning method was proposed to explore the possibility and practicality of neural network applications in medical imaging. The method was used to achieve the goal of judging common pneumonia and even COVID-19 more effectively. Where, the genetic algorithm was taken advantage to optimize the Dropout module, which is essential in neural networks so as to improve the performance of typical neural network models. The experiment results demonstrate that the proposed method shows excellent performance and strong practicability in judging pneumonia, and the application of advanced artificial intelligence technology in the field of medical imaging has broad prospects.

COVID-19;Pneumonia

Neural Networks;Other Topics

External

2. Detection/Diagnosis

X-Ray

2003.10304

Yes

2020-03-23

Preprint

arXiv

attention u-net based adversarial architectures for chest x-ray lung segmentation

Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.

COVID-19;Lung Cancer;Pneumonia;Tuberculosis

Art;Architecture;Lung Diseases

External

Segmentation-only

X-Ray

2007.09695

Yes

2020-07-19

Preprint

arXiv

using deep convolutional neural networks to diagnose covid-19 from chest x-ray images

The COVID-19 epidemic has become a major safety and health threat worldwide. Imaging diagnosis is one of the most effective ways to screen COVID-19. This project utilizes several open-source or public datasets to present an open-source dataset of COVID-19 CXRs, named COVID-19-CXR-Dataset, and introduces a deep convolutional neural network model. The model validates on 740 test images and achieves 87.3% accuracy, 89.67 % precision, and 84.46% recall, and correctly classifies 98 out of 100 COVID-19 x-ray images in test set with more than 81% prediction probability under the condition of 95% confidence interval. This project may serve as a reference for other researchers aiming to advance the development of deep learning applications in medical imaging.

COVID-19

Research Personnel;Neural Networks

External

2. Detection/Diagnosis

X-Ray

32396075

10.1109/TMI.2020.2993291

Yes

2020-05-13

Journal Article

Peer reviewed (PubMed)

deep learning covid-19 features on cxr using limited training data sets

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

COVID-19;COVID-19 Pandemic

IEEE Trans Med Imaging

Coronavirus Infections;Art;Algorithms;Map

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.12.14.20248158

10.1101/2020.12.14.20248158

Yes

2020-12-16

Preprint

medRxiv

transfer learning for covid-19 pneumonia detection and classification in chest x-ray images

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.

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

Transfer Learning;Other Topics

External

2. Detection/Diagnosis

X-Ray

32589673

10.1371/journal.pone.0235187

Yes

PMC7319603

2020-06-27

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

Peer reviewed (PubMed)

new machine learning method for image-based diagnosis of covid-19

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.

COVID-19

PLoS One

Other Topics

External

2. Detection/Diagnosis

X-Ray

2012.15442

Yes

2020-12-30

Preprint

arXiv

survey of the detection and classification of pulmonary lesions via ct and x-ray

In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumonia, and other common lung lesions based on the imaging characteristics of various lesions. Furthermore, this review introduces 26 commonly used public medical image datasets, summarizes the latest technology, and discusses current challenges and future research directions.

COVID-19;COVID-19 Pandemic;Lung Diseases;Pneumonia

Other Topics

N.A.

Review

Multimodal

2010.04936

Yes

2020-10-10

Preprint

arXiv

an empirical study on detecting covid-19 in chest x-ray images using deep learning based methods

Spreading of COVID-19 virus has increased the efforts to provide testing kits. Not only the preparation of these kits had been hard, rare, and expensive but also using them is another issue. Results have shown that these kits take some crucial time to recognize the virus, in addition to the fact that they encounter with 30% loss. In this paper, we have studied the usage of x-ray pictures which are ubiquitous, for the classification of COVID-19 chest Xray images, by the existing convolutional neural networks (CNNs). We intend to train chest x-rays of infected and not infected ones with different CNNs architectures including VGG19, Densnet-121, and Xception. Training these architectures resulted in different accuracies which were much faster and more precise than usual ways of testing.

COVID-19

Other Topics

External

2. Detection/Diagnosis

X-Ray

33769939

10.1109/JBHI.2021.3069169

Yes

PMC8545163

2021-03-27

Journal Article

Peer reviewed (PubMed)

covid-19 in cxr: from detection and severity scoring to patient disease monitoring

This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.

COVID-19;Disease Progression;Pneumonia

IEEE J Biomed Health Inform

Map;Cone-Beam Computed Tomography

External

3. Monitoring/Severity assessment

X-Ray

2004.02640

Yes

2020-04-06

Preprint

arXiv

coronavirus detection and analysis on chest ct with deep learning

The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.

COVID-19;Death

Disease Outbreaks;Radiologists;Other Topics;Cluster Analysis

Self-recorded/clinical

2. Detection/Diagnosis

CT

10.1101/2020.05.10.20097063

10.1101/2020.05.10.20097063

Yes

2020-05-14

Preprint

medRxiv

automatic detection of covid-19 infection from chest x-ray using deep learning

COVID-19 infection has created a panic across the globe in recent times. Early detection of COVID-19 infection can save many lives in the prevailing situation. This virus affects the respiratory system of a person and creates white patchy shadows in the lungs. Deep learning is one of the most effective Artificial Intelligence techniques to analyse chest X-ray images for efficient and reliable COVID-19 screening. In this paper, we have proposed a Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images. To validate the performance of the proposed system, chest X-ray images of more than 150 confirmed COVID-19 patients from the Kaggle data repository are used in the experimentation. The results show that the proposed system identifies the cases with an accuracy of 93%.

COVID-19;Infections

Other Topics

External

2. Detection/Diagnosis

X-Ray

34373554

10.1038/s41598-021-95680-6

Yes

PMC8352869

2021-08-11

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

Peer reviewed (PubMed)

explainable dcnn based chest x-ray image analysis and classification for covid-19 pneumonia detection

To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.

COVID-19;Pneumonia

Sci Rep

Sensitivity and Specificity;Neural Networks

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

10.1101/2020.06.07.20124594

10.1101/2020.06.07.20124594

Yes

2020-06-08

Preprint

medRxiv

early detection of coronavirus cases using chest x-ray images employing machine learning and deep learning approaches

This study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy , AUC , f-measure , sensitivity and specificity as well as the lowest fall out and miss rate respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.

COVID-19

Transfer Learning;Area under Curve

External

2. Detection/Diagnosis

X-Ray

32983909

10.1016/j.matpr.2020.09.352

Yes

PMC7508494

2020-09-29

Journal Article

Peer reviewed (PubMed)

machine learning and image analysis applications in the fight against covid-19 pandemic: datasets research directions challenges and opportunities

COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine Learning and Image Analysis research has been making great progress in many directions in the healthcare field for providing support to subsequent medical diagnosis. In this paper, we have propose three research directions with methodologies in the fight against the pandemic namely: Chest X-Ray (CXR) images classification using deep convolution neural networks with transfer learning to assist diagnosis; Patient Risk prediction of pandemic based on risk factors such as patient characteristics, comorbidities, initial symptoms, vital signs for prognosis of disease; and forecasting of disease spread and case fatality rate using deep neural networks. Further, some of the challenges, open datasets and opportunities are discussed for researchers.

COVID-19 Pandemic

Mater Today Proc

Health Care;Transfer Learning;Research Personnel;Disease Outbreaks;Risk Factors

N.A.

Review

X-Ray

2004.12786

Yes

2020-04-30

Preprint

arXiv

a cascaded learning strategy for robust covid-19 pneumonia chest x-ray screening

We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images.

COVID-19;Pneumonia

Other Topics

External

2. Detection/Diagnosis

X-Ray

33972584

10.1038/s41598-021-88807-2

Yes

PMC8110795

2021-05-12

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

Peer reviewed (PubMed)

covid-classifier: an automated machine learning model to assist in the diagnosis of covid-19 infection in chest x-ray images

Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

COVID-19;Infections;Pneumonia

Sci Rep

Reproducibility of Results;ROC Curve

External

2. Detection/Diagnosis

X-Ray

2010.12967

Yes

2020-10-24

Preprint

arXiv

automated triage of covid-19 from various lung abnormalities using chest ct features

The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

COVID-19;Infections;Pneumonia

Disease Outbreaks;Polymerase Chain Reaction;Area under Curve;Reverse Transcription

External

2. Detection/Diagnosis

CT

10.1101/2020.04.27.20081984

10.1101/2020.04.27.20081984

Yes

2020-05-03

Preprint

medRxiv

distinguishing l and h phenotypes of covid-19 using a single x-ray image

Recent observations have shown that there are two types of COVID-19 response: an H phenotype with high lung elastance and weight, and an L phenotype with low measures1. H-type patients have pneumonia-like thickening of the lungs and require ventilation to survive; L-type patients have clearer lungs that may be injured by mechanical assistance2,3. As treatment protocols differ between the two types, and the number of ventilators is limited, it is vital to classify patients appropriately. To date, the only way to confirm phenotypes is through high-resolution computed tomography2. Here, we identify L- and H-type patients from their frontal chest x-rays using feature-embedded machine learning. We then apply the categorization to multiple images from the same patient, extending it to detect and monitor disease progression and recovery. The results give an immediate criterion for coronavirus triage and provide a methodology for respiratory diseases beyond COVID-19.

COVID-19;Disease Progression;Pneumonia;Respiratory Tract Diseases

Other Topics

External

4. Prognosis/Treatment

X-Ray

10.1101/2020.08.31.20175828

10.1101/2020.08.31.20175828

Yes

2020-09-02

Preprint

medRxiv

covid-19 detection from chest x-ray images using deep learning and convolutional neural networks

Accurate and efficient diagnosis of potential COVID-19 patients is vital in the fight against the current pandemic. However, even the gold-standard COVID-19 test, reverse transcription polymerase chain reaction, suffers from a high false negative rate and a turnaround time of up to one week, preventing the infected from accessing the timely care they require, and impeding efforts to isolate positive cases. To address these shortcomings, this study develops a machine learning model based on the DenseNet-201 deep convolutional neural network, that can classify COVID-19 from chest radiographs in less than one minute and far more accurately than conventional tests (F1-score: 0.96; precision: 0.95; recall: 0.98). It uses a significantly larger dataset and more control classes than previously published models, demonstrating the promise of a machine learning approach for accurate and efficient COVID-19 screening. A live web application of the trained model can be accessed at /.

COVID-19

Polymerase Chain Reaction;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

33100403

10.1016/j.patcog.2020.107700

Yes

PMC7568501

2020-10-27

Journal Article

Peer reviewed (PubMed)

metacovid: a siamese neural network framework with contrastive loss for n-shot diagnosis of covid-19 patients

Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep meta learning has been proposed in this research to accelerate analysis of chest X-ray (CXR) images in automatic detection of COVID-19 cases. We present a synergistic approach to integrate contrastive learning with a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations and leverage a Siamese network for final classification of COVID-19 cases. We validate the effectiveness of our proposed model using two publicly available datasets comprising images from normal, COVID-19 and other pneumonia infected categories. Our model achieves 95.6% accuracy and AUC of 0.97 in diagnosing COVID-19 from CXR images even with a limited number of training samples.

COVID-19;Infections;Pneumonia

Pattern Recognit

Other Topics

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.05.14.20101972

10.1101/2020.05.14.20101972

Yes

2020-10-06

Preprint

medRxiv

integration of clinical characteristics lab tests and a deep learning ct scan analysis to predict severity of hospitalized covid-19 patients

The SARS-COV-2 pandemic has put pressure on Intensive Care Units, so that identifying predictors of disease severity is a priority. We collected 58 clinical and biological variables, chest CT scan data (506,341 images), and radiology reports from 1,003 coronavirus-infected patients from two French hospitals. We trained a deep learning model based on CT scans to predict severity; this model was more discriminative than a radiologist quantification of disease extent. We showed that neural network analysis of CT-scan brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP). To provide a multimodal severity score, we developed AI-severity that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) as well as the CT deep learning model. When comparing AI-severity with 11 existing scores for severity, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

COVID-19

Other Topics

Self-recorded/clinical

3. Monitoring/Severity assessment

CT

32604588

10.3233/SHTI200481

Yes

2020-07-02

Journal Article

Peer reviewed (PubMed)

setting up an easy-to-use machine learning pipeline for medical decision support: a case study for covid-19 diagnosis based on deep learning with ct scans

Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients. The aim of the present study was to evaluate the performance of an automated machine learning algorithm in the diagnosis of Covid-19 pneumonia using chest CT scans. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. The method's average precision was 0.932. We suggest that auto-ML platforms help users with limited ML expertise train image recognition models by only uploading the examined dataset and performing some basic settings. Such methods could deliver significant potential benefits for patients in the future by allowing for earlier disease detection and care.

COVID-19;Pneumonia

Stud Health Technol Inform

Other Topics

External

2. Detection/Diagnosis

CT

35431611

10.1007/s11042-022-12156-z

Yes

PMC8989406

2022-04-19

Journal Article

Peer reviewed (PubMed)

covid-cxnet: detecting covid-19 in frontal chest x-ray images using deep learning

One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

COVID-19;Pneumonia;Pneumonia, Viral

Multimed Tools Appl

Transfer Learning;Other Topics

External

2. Detection/Diagnosis

X-Ray

32796848

10.1038/s41467-020-17971-2

Yes

PMC7429815

2020-08-17

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

Peer reviewed (PubMed)

artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

COVID-19;Lung Diseases;Pneumonia

Nat Commun

Coronavirus Infections;COVID-19 Testing

Self-recorded/clinical

2. Detection/Diagnosis

CT

32959234

10.1007/s12539-020-00393-5

Yes

PMC7505483

2020-09-23

Journal Article

Peer reviewed (PubMed)

covid19xraynet: a two-step transfer learning model for the covid-19 detecting problem based on a limited number of chest x-ray images

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.

COVID-19;Severe Acute Respiratory Syndrome

Interdiscip Sci

Radiography;Coronavirus Infections;Transfer Learning;Algorithms;Disease Outbreaks;COVID-19 Testing;Lung;Neural Networks;Tomography

External

2. Detection/Diagnosis

X-Ray

32356760

10.1109/RBME.2020.2990959

Yes

2020-05-02

Journal Article;Review

Peer reviewed (PubMed)

the role of imaging in the detection and management of covid-19: a review

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

COVID-19;Death;Infections;Pneumonia;Severe Acute Respiratory Syndrome

IEEE Rev Biomed Eng

Magnetic Resonance Imaging;Pneumonia;Lung;Ultrasonography;Tomography;Review

External

Review

Multimodal

32536759

10.1016/j.chaos.2020.109944

Yes

PMC7254021

2020-06-17

Journal Article

Peer reviewed (PubMed)

application of deep learning for fast detection of covid-19 in x-rays using ncovnet

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

COVID-19;COVID-19 Pandemic

Chaos Solitons Fractals

Other Topics

External

2. Detection/Diagnosis

X-Ray

32834523

10.1007/s00138-020-01101-5

Yes

PMC7386599

2020-08-25

Journal Article

Peer reviewed (PubMed)

deep learning applications in pulmonary medical imaging: recent updates and insights on covid-19

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

COVID-19;COVID-19 Pandemic;Cancer;Infections;Lung Diseases

Mach Vis Appl

Coronavirus Infections;Art;Algorithms;Lung Diseases

N.A.

Review

Multimodal

10.1101/2020.04.13.20063479

10.1101/2020.04.13.20063479

Yes

2020-04-17

Preprint

medRxiv

machine learning analysis of chest ct scan images as a complementary digital test of coronavirus (covid-19) patients

This paper reports on the development and performance of machine learning schemes for the analysis of Chest CT Scan images of Coronavirus COVID-19 patients and demonstrates significant success in efficiently and automatically testing for COVID-19 infection. In particular, an innovative frequency domain algorithm, to be called FFT-Gabor scheme, will be shown to predict in almost real-time the state of the patient with an average accuracy of 95.37%, sensitivity 95.99% and specificity 94.76%. The FFT-Gabor scheme is adequately informative in that clinicians can visually examine the FFT-Gabor feature to support their final diagnostic.The proposed FFT-Gabor scheme is an automatic machine learning scheme that works in real time and achieves significantly high accuracy with very low false negative, and can provide supporting evidences of the predicted decision by visually displaying the final features upon which decision is made. This scheme will be most beneficial when used in addition to the RT-PCR swab test of non-symptomatic cases.

COVID-19;Infections

Coronavirus Infections;Polymerase Chain Reaction

External

2. Detection/Diagnosis

CT

10.1101/2020.10.13.20178483

10.1101/2020.10.13.20178483

Yes

2020-10-14

Preprint

medRxiv

automated chest radiograph diagnosis: a twofer for tuberculosis and covid-19

Coronavirus disease (Covid 19) and Tuberculosis (TB) are two challenges the world is facing. TB is a pandemic which has challenged mankind for ages and Covid 19 is a recent onset fast spreading pandemic. We study these two conditions with focus on Artificial Intelligence (AI) based imaging, the role of digital chest x-ray and utility of end to end platform to improve turnaround times. Using artificial intelligence assisted technology for triage and creation of structured radiology reports using an end to end platform can ensure quick diagnosis. Changing dynamics of TB screening in the times of Covid 19 pandemic have resulted in bottlenecks for TB diagnosis. The paper tries to outline two types of use cases, one is COVID-19 screening in a hospital-based scenario and the other is TB screening project in mobile van setting and discusses the learning of these models which have both used AI for prescreening and generating structured radiology reports.

COVID-19;COVID-19 Pandemic;Tuberculosis

Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

X-Ray

10.1101/2020.05.01.20088211

10.1101/2020.05.01.20088211

Yes

2020-06-18

Preprint

medRxiv

classification of covid-19 from chest x-ray images using deep convolutional neural networks

The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID‐ 19 pneumonia patients using digital chest x‐ ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID‐ 19, 1345 viral pneumonia and 1341 normal chest x‐ ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and gives a classification accuracy of more than 98% (training accuracy of 97% and validation accuracy of 93%). The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.

COVID-19;COVID-19 Pandemic;Pneumonia;Pneumonia, Viral

Transfer Learning;Other Topics

External

2. Detection/Diagnosis

Multimodal

2004.03747

Yes

2020-04-18

Preprint

arXiv

covid_mtnet: covid-19 detection with multi-task deep learning approaches

COVID-19 is currently one the most life-threatening problems around the world. The fast and accurate detection of the COVID-19 infection is essential to identify, take better decisions and ensure treatment for the patients which will help save their lives. In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods. Both X-ray and CT scan images are considered to evaluate the proposed technique. We employ our Inception Residual Recurrent Convolutional Neural Network with Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network model for segmenting the regions infected by COVID-19. The detection model shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in CT-images. A novel quantitative analysis strategy is also proposed in this paper to determine the percentage of infected regions in X-ray and CT images. The qualitative and quantitative results demonstrate promising results for COVID-19 detection and infected region localization.

COVID-19;Infections

Transfer Learning;Other Topics

External

2. Detection/Diagnosis

Multimodal

2007.14318

Yes

2020-08-29

Preprint

arXiv

covmunet: a multiple loss approach towards detection of covid-19 from chest x-ray

The recent outbreak of COVID-19 has halted the whole world, bringing a devastating effect on public health, global economy, and educational systems. As the vaccine of the virus is still not available, the most effective way to combat the virus is testing and social distancing. Among all other detection techniques, the Chest X-ray (CXR) based method can be a good solution for its simplicity, rapidity, cost, efficiency, and accessibility. In this paper, we propose CovMUNET, which is a multiple loss deep neural network approach to detect COVID-19 cases from CXR images. Extensive experiments are performed to ensure the robustness of the proposed algorithm and the performance is evaluated in terms of precision, recall, accuracy, and F1-score. The proposed method outperforms the state-of-the-art approaches with an accuracy of 96.97% for 3-class classification (COVID-19 vs normal vs pneumonia) and 99.41% for 2-class classification (COVID vs non-COVID). The proposed neural architecture also successfully detects the abnormality in CXR images.

COVID-19;Pneumonia

Public Health;Art;Algorithms;Architecture;Disease Outbreaks

External

2. Detection/Diagnosis

X-Ray

33208927

10.1038/s41551-020-00633-5

Yes

PMC7723858

2020-11-20

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

Peer reviewed (PubMed)

open resource of clinical data from patients with pneumonia for the prediction of covid-19 outcomes via deep learning

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

COVID-19;Pneumonia;Severe Acute Respiratory Syndrome;Virus Diseases

Nat Biomed Eng

Algorithms;ROC Curve

Self-recorded/clinical

4. Prognosis/Treatment

CT

32742318

10.3892/etm.2020.8797

Yes

PMC7388253

2020-08-04

Journal Article

Peer reviewed (PubMed)

interpretable artificial intelligence framework for covid-19 screening on chest x-rays

COVID-19 has led to an unprecedented healthcare crisis with millions of infected people across the globe often pushing infrastructures, healthcare workers and entire economies beyond their limits. The scarcity of testing kits, even in developed countries, has led to extensive research efforts towards alternative solutions with high sensitivity. Chest radiological imaging paired with artificial intelligence (AI) can offer significant advantages in diagnosis of novel coronavirus infected patients. To this end, transfer learning techniques are used for overcoming the limitations emanating from the lack of relevant big datasets, enabling specialized models to converge on limited data, as in the case of X-rays of COVID-19 patients. In this study, we present an interpretable AI framework assessed by expert radiologists on the basis on how well the attention maps focus on the diagnostically-relevant image regions. The proposed transfer learning methodology achieves an overall area under the curve of 1 for a binary classification problem across a 5-fold training/testing dataset.

COVID-19

Exp Ther Med

Health Care;Transfer Learning;Research Personnel;Health;Map

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.07.08.20149161

10.1101/2020.07.08.20149161

Yes

2020-07-10

Preprint

medRxiv

covidpen: a novel covid-19 detection model using chest x-rays and ct scans

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome (SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose COVIDPEN - a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

COVID-19;Severe Acute Respiratory Syndrome

Transfer Learning;Polymerase Chain Reaction;Tomography;Other Topics

External

2. Detection/Diagnosis

Multimodal

33643491

10.1007/s13278-021-00731-5

Yes

PMC7903408

2021-03-02

Journal Article

Peer reviewed (PubMed)

synthesis of covid-19 chest x-rays using unpaired image-to-image translation

Motivated by the lack of publicly available datasets of chest radiographs of positive patients with coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. Our contributions are twofold. First, we show considerable performance improvements on COVID-19 detection using various deep learning architectures when employing synthetic images as additional training set. Second, we show how our image synthesis method can serve as a data anonymization tool by achieving comparable detection performance when trained only on synthetic data. In addition, the proposed data generation framework offers a viable solution to the COVID-19 detection in particular, and to medical image classification tasks in general. Our publicly available benchmark dataset (GitHub) consists of 21,295 synthetic COVID-19 chest X-ray images. The insights gleaned from this dataset can be used for preventive actions in the fight against the COVID-19 pandemic.

COVID-19;COVID-19 Pandemic

Soc Netw Anal Min

X-Rays;Translations

External

5. Post-hoc

X-Ray

2004.05436

Yes

2020-04-11

Preprint

arXiv

detection of covid-19 from chest x-ray images using artificial intelligence: an early review

In 2019, the entire world is facing a situation of health emergency due to a newly emerged coronavirus (COVID-19). Almost 196 countries are affected by covid-19, while USA, Italy, China, Spain, Iran, and France have the maximum active cases of COVID-19. The issues, medical and healthcare departments are facing in delay of detecting the COVID-19. Several artificial intelligence based system are designed for the automatic detection of COVID-19 using chest x-rays. In this article we will discuss the different approaches used for the detection of COVID-19 and the challenges we are facing. It is mandatory to develop an automatic detection system to prevent the transfer of the virus through contact. Several deep learning architecture are deployed for the detection of COVID-19 such as ResNet, Inception, Googlenet etc. All these approaches are detecting the subjects suffering with pneumonia while its hard to decide whether the pneumonia is caused by COVID-19 or due to any other bacterial or fungal attack.

COVID-19;Pneumonia

Health Care;Other Topics

External

Review

X-Ray

32501424

10.1016/j.imu.2020.100360

Yes

PMC7255267

2020-06-06

Journal Article

Peer reviewed (PubMed)

a modified deep convolutional neural network for detecting covid-19 and pneumonia from chest x-ray images based on the concatenation of xception and resnet50v2

In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.

COVID-19;Pneumonia

Inform Med Unlocked

Transfer Learning;Other Topics

External

2. Detection/Diagnosis

X-Ray

33961635

10.1371/journal.pone.0250952

Yes

PMC8104381

2021-05-08

Journal Article

Peer reviewed (PubMed)

ai-corona: radiologist-assistant deep learning framework for covid-19 diagnosis in chest ct scans

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework's diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona's assistance.

COVID-19;Infections;Pneumonia

PLoS One

Disease Outbreaks;Polymerase Chain Reaction;Radiologists;Other Topics;ROC Curve;Area under Curve

Self-recorded/clinical

2. Detection/Diagnosis

CT

33014121

10.1155/2020/9756518

Yes

PMC7519983

2020-10-06

Journal Article;Review

Peer reviewed (PubMed)

review on diagnosis of covid-19 from chest ct images using artificial intelligence

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms "deep learning", "neural networks", "COVID-19", and "chest CT". At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

COVID-19;Pneumonia

Comput Math Methods Med

Radiography;Coronavirus Infections;COVID-19 Testing;Sensitivity and Specificity;Neural Networks

N.A.

Review

CT

2007.05592

Yes

2020-07-05

Preprint

arXiv

experiments of federated learning for covid-19 chest x-ray images

AI plays an important role in COVID-19 identification. Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. Collecting such training data was a major challenge. To a certain extent, this has caused a lack of sufficient data samples when performing deep learning approaches to detect COVID-19. Federated Learning is an available way to address this issue. It can effectively address the issue of data silos and get a shared model without obtaining local data. In the work, we propose the use of federated learning for COVID-19 data training and deploy experiments to verify the effectiveness. And we also compare performances of four popular models (MobileNet, ResNet18, MoblieNet, and COVID-Net) with the federated learning framework and without the framework. This work aims to inspire more researches on federated learning about COVID-19.

COVID-19;Infections

Other Topics

External

2. Detection/Diagnosis

X-Ray

2008.02866

Yes

2020-08-15

Preprint

arXiv

improving explainability of image classification in scenarios with class overlap: application to covid-19 and pneumonia

Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions. In the image classification domain, generalization can be assessed through accuracy, sensitivity, and specificity. Explainability can be assessed by how well the model localizes the object of interest within an image. However, both generalization and explainability through localization are degraded in scenarios with significant overlap between classes. We propose a method based on binary expert networks that enhances the explainability of image classifications through better localization by mitigating the model uncertainty induced by class overlap. Our technique performs discriminative localization on images that contain features with significant class overlap, without explicitly training for localization. Our method is particularly promising in real-world class overlap scenarios, such as COVID-19 and pneumonia, where expertly labeled data for localization is not readily available. This can be useful for early, rapid, and trustworthy screening for COVID-19.

COVID-19;Pneumonia

Other Topics

External

5. Post-hoc

X-Ray

33967656

10.1016/j.inffus.2021.04.008

Yes

PMC8086233

2021-05-11

Journal Article

Peer reviewed (PubMed)

a critic evaluation of methods for covid-19 automatic detection from x-ray images

In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose.

COVID-19

Inf Fusion

Black Americans;Research Personnel;Techniques;X-Rays;Dataset;Paper

External

2. Detection/Diagnosis

X-Ray

10.1101/2020.05.06.20092874

10.1101/2020.05.06.20092874

Yes

2020-05-08

Preprint

medRxiv

prognet: covid-19 prognosis using recurrent and convolutional neural networks

, Humanity is facing nowadays a dramatic pandemic episode with the Coronavirus propagation over all continents. The Covid-19 disease is still not well characterized, and many research teams all over the world are working on either therapeutic or vaccination issues. Massive testing is one of the main recommendations. In addition to laboratory tests, imagery-based tools are being widely investigated. Artificial intelligence is therefore contributing to the efforts made to face this pandemic phase. Regarding patients in hospitals, it is important to monitor the evolution of lung pathologies due to the virus. A prognosis is therefore of great interest for doctors to adapt their care strategy. In this paper, we propose a method for Covid-19 prognosis based on deep learning architectures. The proposed method is based on the combination of a convolutional and recurrent neural networks to classify multi-temporal chest X-ray images and predict the evolution of the observed lung pathology. When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%.

COVID-19

Other Topics

External

4. Prognosis/Treatment

X-Ray

10.1101/2020.11.08.20227819

10.1101/2020.11.08.20227819

Yes

2020-11-12

Preprint

medRxiv

detection of covid-19 disease from chest x-ray images: a deep transfer learning framework

World economy as well as public health have been facing a devastating effect caused by the disease termed as Coronavirus (COVID-19). A significant step of COVID-19 affected patient’s treatment is the faster and accurate detection of the disease which is the motivation of this study. In this paper, implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Most importantly, the model has shown a significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00 and Specificity = 1.00). Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.

COVID-19;Pneumonia, Viral

Public Health;Transfer Learning

External

2. Detection/Diagnosis

X-Ray

2004.09750

Yes

2021-03-21

Preprint

arXiv

miniseg: an extremely minimum network for efficient covid-19 segmentation

The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.

COVID-19

Other Topics

External

Segmentation-only

CT

32700269

10.1007/s11356-020-10133-3

Yes

PMC7375456

2020-07-24

Journal Article

Peer reviewed (PubMed)

drawing insights from covid-19-infected patients using ct scan images and machine learning techniques: a study on 200 patients

As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology.

COVID-19;Pneumonia;Pneumonia, Viral

Environ Sci Pollut Res Int

Coronavirus Infections;Polymerase Chain Reaction

External

2. Detection/Diagnosis

CT

33169050

10.1007/s00138-020-01128-8

Yes

PMC7609373

2020-11-11

Journal Article

Peer reviewed (PubMed)

a five-layer deep convolutional neural network with stochastic pooling for chest ct-based covid-19 diagnosis

Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: We introduced stochastic pooling to replace average pooling and max pooling; We combined conv layer with batch normalization layer and obtained the conv block (CB); We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% %, a specificity of 94.00% %, and an accuracy of 93.64% %, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images.

COVID-19

Mach Vis Appl

Other Topics

External

2. Detection/Diagnosis

CT

10.1101/2020.12.20.20248582

10.1101/2020.12.20.20248582

Yes

2020-12-23

Preprint

medRxiv

rapid covid-19 diagnosis using deep learning of the computerized tomography scans

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

Ache;COVID-19;Coronavirus Infections;Cough;Infections;Sore Throat

Other Topics

External

2. Detection/Diagnosis

CT

33171345

10.1016/j.media.2020.101860

Yes

PMC7558247

2020-11-11

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

Peer reviewed (PubMed)

ai-driven quantification staging and outcome prediction of covid-19 pneumonia

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.

COVID-19;Pneumonia

Med Image Anal

Drug;Architecture;Neural Networks

Self-recorded/clinical

4. Prognosis/Treatment

CT

2003.10769

Yes

2020-03-27

Preprint

arXiv

estimating uncertainty and interpretability in deep learning for coronavirus (covid-19) detection

Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowing how much confidence there is in a computer-based medical diagnosis is essential for gaining clinicians trust in the technology and therefore improve treatment. Today, the 2019 Coronavirus (SARS-CoV-2) infections are a major healthcare challenge around the world. Detecting COVID-19 in X-ray images is crucial for diagnosis, assessment and treatment. However, diagnostic uncertainty in the report is a challenging and yet inevitable task for radiologist. In this paper, we investigate how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is highly correlates with accuracy of prediction. We believe that the availability of uncertainty-aware deep learning solution will enable a wider adoption of Artificial Intelligence (AI) in a clinical setting.

COVID-19

Coronavirus Infections;Art;Health Care

External

Segmentation-only

X-Ray

33603047

10.1038/s41598-021-83424-5

Yes

PMC7892869

2021-02-20

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

Peer reviewed (PubMed)

assisting scalable diagnosis automatically via ct images in the combat against covid-19

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

COVID-19;Infections;Pneumonia

Sci Rep

Polymerase Chain Reaction;Other Topics;Retrospective Studies;Reverse Transcription

Self-recorded/clinical

2. Detection/Diagnosis

CT

32436845

10.5152/dir.2019.20294

Yes

PMC7490030

2020-05-22

Journal Article;Review

Peer reviewed (PubMed)

a review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019

The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.

COVID-19;Pneumonia

Diagn Interv Radiol

Other Topics

External

Review

Multimodal

2008.11639

Yes

2020-10-09

Preprint

arXiv

a comparison of deep machine learning algorithms in covid-19 disease diagnosis

The aim of the work is to use deep neural network models for solving the problem of image recognition. These days, every human being is threatened by a harmful coronavirus disease, also called COVID-19 disease. The spread of coronavirus affects the economy of many countries in the world. To find COVID-19 patients early is very essential to avoid the spread and harm to society. Pathological tests and Chromatography (CT) scans are helpful for the diagnosis of COVID-19. However, these tests are having drawbacks such as a large number of false positives, and cost of these tests are so expensive. Hence, it requires finding an easy, accurate, and less expensive way for the detection of the harmful COVID-19 disease. Chest-x-ray can be useful for the detection of this disease. Therefore, in this work chest, x-ray images are used for the diagnosis of suspected COVID-19 patients using modern machine learning techniques. The analysis of the results is carried out and conclusions are made about the effectiveness of deep machine learning algorithms in image recognition problems.

COVID-19

Neural Networks;Other Topics

External

2. Detection/Diagnosis

X-Ray

33286289

10.3390/e22050517

Yes

PMC7517011

2020-12-09

Journal Article

Peer reviewed (PubMed)

classification of covid-19 coronavirus pneumonia and healthy lungs in ct scans using q-deformed entropy and deep learning features

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists' efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

COVID-19;Pneumonia

Entropy (Basel)

Health;Radiologists;Entropy

External

2. Detection/Diagnosis

CT

32971995

10.3390/ijerph17186933

Yes

PMC7557723

2020-09-26

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

Peer reviewed (PubMed)

unveiling covid-19 from chest x-ray with deep learning: a hurdles race with small data

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

COVID-19

Int J Environ Res Public Health

Coronavirus Infections;Transfer Learning

External

2. Detection/Diagnosis

X-Ray

rs-1396136

10.21203/rs.3.rs-1396136/v1

Yes

2022-03-11

Preprint

Research Square

exploration of interpretability techniques for deep covid-19 classification using chest x-ray images

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniae and healthy subjects using Chest X-Ray images. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable models.

COVID-19

Disease Outbreaks;Other Topics

External

2. Detection/Diagnosis

X-Ray

2003.14363

Yes

2020-03-31

Preprint

arXiv

automated methods for detection and classification pneumonia based on x-ray images using deep learning

Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray and CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).

COVID-19;Pneumonia

Other Topics

External

2. Detection/Diagnosis

X-Ray

32760732

10.3389/fmed.2020.00427

Yes

PMC7371960

2020-08-08

Journal Article

Peer reviewed (PubMed)

deep learning-based decision-tree classifier for covid-19 diagnosis from chest x-ray imaging

The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.

COVID-19;Pneumonia;Tuberculosis

Front Med (Lausanne)

Pneumonia;Polymerase Chain Reaction;Decision Trees;Reverse Transcription

External

2. Detection/Diagnosis

X-Ray

33980980

10.1038/s41746-021-00453-0

Yes

PMC8115328

2021-05-14

Journal Article

Peer reviewed (PubMed)

an artificial intelligence system for predicting the deterioration of covid-19 patients in the emergency department

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

COVID-19;COVID-19 Pandemic

NPJ Digit Med

Other Topics

Self-recorded/clinical

4. Prognosis/Treatment

X-Ray

2007.01108

Yes

2020-06-30

Preprint

arXiv

evaluation of contemporary convolutional neural network architectures for detecting covid-19 from chest radiographs

Interpreting chest radiograph, a.ka. chest x-ray, images is a necessary and crucial diagnostic tool used by medical professionals to detect and identify many diseases that may plague a patient. Although the images themselves contain a wealth of valuable information, their usefulness may be limited by how well they are interpreted, especially when the reviewing radiologist may be fatigued or when or an experienced radiologist is unavailable. Research in the use of deep learning models to analyze chest radiographs yielded impressive results where, in some instances, the models outperformed practicing radiologists. Amidst the COVID-19 pandemic, researchers have explored and proposed the use of said deep models to detect COVID-19 infections from radiographs as a possible way to help ease the strain on medical resources. In this study, we train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions, find issues that discount the impressive model performances proposed by contemporary studies on this subject, and propose methodologies to train models that yield more reliable results.. Code, scripts, pre-trained models, and visualizations are available at GitHub

COVID-19;COVID-19 Pandemic;Infections;Plague

Other Topics

External

2. Detection/Diagnosis

X-Ray

32568679

10.1016/j.compbiomed.2020.103805

Yes

PMC7202857

2020-06-23

Journal Article

Peer reviewed (PubMed)

covid-19 detection using deep learning models to exploit social mimic optimization and structured chest x-ray images using fuzzy color and stacking approaches

Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

COVID-19;Pneumonia;Respiratory Tract Infections

Comput Biol Med

Other Topics

External

2. Detection/Diagnosis

X-Ray

2010.09456

Yes

2020-10-19

Preprint

arXiv

gasnet: weakly-supervised framework for covid-19 lesion segmentation

Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients. Due to the complex shapes and varied appearances of lesions, a large number of voxel-level labeled samples are generally required to train a lesion segmentation network, which is a main bottleneck for developing deep learning based medical image segmentation algorithms. In this paper, we propose a weakly-supervised lesion segmentation framework by embedding the Generative Adversarial training process into the Segmentation Network, which is called GASNet. GASNet is optimized to segment the lesion areas of a COVID-19 CT by the segmenter, and to replace the abnormal appearance with a generated normal appearance by the generator, so that the restored CT volumes are indistinguishable from healthy CT volumes by the discriminator. GASNet is supervised by chest CT volumes of many healthy and COVID-19 subjects without voxel-level annotations. Experiments on three public databases show that when using as few as one voxel-level labeled sample, the performance of GASNet is comparable to fully-supervised segmentation algorithms trained on dozens of voxel-level labeled samples.

COVID-19

Other Topics

External

Segmentation-only

CT

10.1101/2020.05.04.20090779

10.1101/2020.05.04.20090779

Yes

2020-05-08

Preprint

medRxiv

mantiscovid: rapid x-ray chest radiograph and mortality rate evaluation with artificial intelligence for covid-19

The novel coronavirus pandemic has negative impacts over the health, economy and well-being of the global population. This negative effect is growing with the high spreading rate of the virus. The most critical step to prevent the spreading of the virus is pre-screening and early diagnosis of the individuals. This results in quaranteeing the patients not to effect the healthy population. COVID-19 is the name of the disease caused by the novel coronavirus. It has a high infection rate and it is urgent to diagnose many patients as we can to prevent the spread of the virus at the early stage. Rapid diagnostic tools development is urgent to save lives. MantisCOVID is a cloud-based pre-diagnosis tool to be accessed from the internet. This tool delivers a rapid screening test by analyzing the X-ray Chest Radiograph scans via Artificial Intelligence (AI) and it also evaluates the mortality rate of patients with the synthesis of the patient’s history with the machine learning methods. This study reveals the methods used over the platform and evaluation of the algorithms via open datasets.

COVID-19;Infections

Coronavirus Infections;Diagnostic Tests;COVID-19 Testing

External

2. Detection/Diagnosis

X-Ray

32849861

10.1155/2020/8828855

Yes

PMC7439162

2020-08-28

Journal Article

Peer reviewed (PubMed)

covid-19 deep learning prediction model using publicly available radiologist-adjudicated chest x-ray images as training data: preliminary findings

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.

COVID-19

Int J Biomed Imaging

Other Topics

External

2. Detection/Diagnosis

X-Ray

32619398

10.1080/07391102.2020.1788642

Yes

2020-07-04

Journal Article

Peer reviewed (PubMed)

classification of the covid-19 infected patients using densenet201 based deep transfer learning

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 or COVID . The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches.Communicated by Ramaswamy H. Sarma.

COVID-19

J Biomol Struct Dyn

Transfer Learning;Architecture;Neural Networks;Tomography

External

2. Detection/Diagnosis

CT

33363252

10.1016/j.imu.2020.100505

Yes

PMC7752710

2020-12-29

Journal Article

Peer reviewed (PubMed)

emcnet: automated covid-19 diagnosis from x-ray images using convolutional neural network and ensemble of machine learning classifiers

Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In this paper, an automated detection scheme named EMCNet was proposed to identify COVID-19 patients by evaluating chest X-ray images. A convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19. With the extracted features, binary machine learning classifiers (random forest, support vector machine, decision tree, and AdaBoost) were developed for the detection of COVID-19. Finally, these classifiers' outputs were combined to develop an ensemble of classifiers, which ensures better results for the dataset of various sizes and resolutions. In comparison with other recent deep learning-based systems, EMCNet showed better performance with 98.91% accuracy, 100% precision, 97.82% recall, and 98.89% F1-score. The system could maintain its great importance on the automatic detection of COVID-19 through instant detection and low false negative rate.

COVID-19

Inform Med Unlocked

Health Care;Research Personnel;Support Vector Machine;Decision Trees;Random Forest

External

2. Detection/Diagnosis

X-Ray

32337662

10.1007/s10096-020-03901-z

Yes

PMC7183816

2020-04-28

Journal Article

Peer reviewed (PubMed)

classification of covid-19 patients from chest ct images using multi-objective differential evolution-based convolutional neural networks

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

COVID-19;Infections

Eur J Clin Microbiol Infect Dis

Coronavirus Infections;COVID-19 Testing;Sensitivity and Specificity;Polymerase Chain Reaction;Neural Networks;Paper;Reverse Transcription

External

2. Detection/Diagnosis

CT

33041409

10.1016/j.patrec.2020.10.001

Yes

PMC7532353

2020-10-13

Journal Article

Peer reviewed (PubMed)

a light cnn for detecting covid-19 from ct scans of the chest

Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.03% with an improvement of about 3.2% in the first dataset arrangement and of about 2.1% in the second dataset arrangement. The obtained gain, though of low entity, can be really important in medical diagnosis and, in particular, for Covid-19 scenario. Also the average classification time on a high-end workstation, 1.25 s, is very competitive with respect to that of more complex CNN designs, 13.41 s, witch require pre-processing. The proposed CNN can be executed on medium-end laptop without GPU acceleration in 7.81 s: this is impossible for methods requiring GPU acceleration. The performance of the method can be further improved with efficient pre-processing strategies for witch GPU acceleration is not necessary.

COVID-19;Pneumonia

Pattern Recognit Lett

Other Topics

External

2. Detection/Diagnosis

CT

33440674

10.3390/s21020455

Yes

PMC7828058

2021-01-15

Journal Article

Peer reviewed (PubMed)

explainable covid-19 detection using chest ct scans and deep learning

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.

COVID-19

Sensors (Basel)

Transfer Learning;Algorithms;Architecture;Sensitivity and Specificity;Neural Networks;Tomography

External

2. Detection/Diagnosis

CT

10.1101/2020.11.08.20228080

10.1101/2020.11.08.20228080

Yes

2020-11-12

Preprint

medRxiv

automatic covid-19 detection from chest radiographic images using convolutional neural network

The global pandemic of the novel coronavirus that started in Wuhan, China has affected more than 2 million people worldwide and caused more than 130,000 tragic deaths. To date, the COVID-19 virus is still spreading and affecting thousands of people. The main problem with testing for COVID-19 is that there are very few test kits available for a large number of affected or suspicious individuals. This leads to the need for automatic detection systems that use artificial intelligence. Deep learning is one of the most powerful AI tools available, so we recommend creating a convolutional neural network to detect COVID-19 positive patients from chest radiographs. According to previous studies, lung X-rays of COVID-19-positive patients show obvious characteristics, so this is a reliable method for testing patients, because X-ray examination of suspicious patients is easier than rt-PCR. Our model has been trained with 820 chest radiographic images (excluding data augmentation) collected from 3 databases, with a classification accuracy of 99.61% (training accuracy of 99.59%), sensitivity of 99.21% and specificity of 99.29 %, proved that our model has become a reliable COVID-19 detector.

COVID-19;Death

Polymerase Chain Reaction;Other Topics

External

2. Detection/Diagnosis

X-Ray

2004.05645

Yes

2020-04-12

Preprint

arXiv

residual attention u-net for automated multi-class segmentation of covid-19 chest ct images

The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. However, there is still lack of studies on effectively quantifying the lung infection caused by COVID-19. As a basic but challenging task of the diagnostic framework, segmentation plays a crucial role in accurate quantification of COVID-19 infection measured by computed tomography (CT) images. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. Specifically, we use the Aggregated Residual Transformations to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, we validate the efficacy of the proposed algorithm in comparison with other competing methods. Experimental results demonstrate the outstanding performance of our algorithm for automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep leaning-based segmentation tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection in CT images.

COVID-19;Infections

Other Topics

External

Segmentation-only

CT

33161334

10.1016/j.compbiomed.2020.104092

Yes

PMC7591316

2020-11-09

Journal Article

Peer reviewed (PubMed)

the importance of standardisation - covid-19 ct and radiograph image data stock for deep learning purpose

With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. In this work, we propose a new dataset which we call COVID-19 CT and Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. Moreover, it is supplemented with a carefully defined split. The aim of COVID-19 CT and Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models.

COVID-19;Pneumonia

Comput Biol Med

Other Topics

Self-recorded/clinical

2. Detection/Diagnosis

CT

32397844

10.1080/07391102.2020.1767212

Yes

PMC7256347

2020-05-14

Journal Article

Peer reviewed (PubMed)

using x-ray images and deep learning for automated detection of coronavirus disease

Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray and CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).Communicated by Ramaswamy H. Sarma.

COVID-19;Confusion;Death;Pneumonia;Pneumonia, Bacterial

J Biomol Struct Dyn

Other Topics

External

2. Detection/Diagnosis

X-Ray

2007.05494

Yes

2020-07-01

Preprint

arXiv

automatic detection of covid-19 cases on x-ray images using convolutional neural networks

In recent months the world has been surprised by the rapid advance of COVID-19. In order to face this disease and minimize its socio-economic impacts, in addition to surveillance and treatment, diagnosis is a crucial procedure. However, the realization of this is hampered by the delay and the limited access to laboratory tests, demanding new strategies to carry out case triage. In this scenario, deep learning models are being proposed as a possible option to assist the diagnostic process based on chest X-ray and computed tomography images. Therefore, this research aims to automate the process of detecting COVID-19 cases from chest images, using convolutional neural networks (CNN) through deep learning techniques. The results can contribute to expand access to other forms of detection of COVID-19 and to speed up the process of identifying this disease. All databases used, the codes built, and the results obtained from the models' training are available for open access. This action facilitates the involvement of other researchers in enhancing these models since this can contribute to the improvement of results and, consequently, the progress in confronting COVID-19.

COVID-19

Other Topics

External

2. Detection/Diagnosis

X-Ray

2004.03698

Yes

2020-04-07

Preprint

arXiv

coronavirus (covid-19) classification using deep features fusion and ranking technique

Coronavirus (COVID-19) emerged towards the end of 2019. World Health Organization (WHO) was identified it as a global epidemic. Consensus occurred in the opinion that using Computerized Tomography (CT) techniques for early diagnosis of pandemic disease gives both fast and accurate results. It was stated by expert radiologists that COVID-19 displays different behaviours in CT images. In this study, a novel method was proposed as fusing and ranking deep features to detect COVID-19 in early phase. 16x16 (Subset-1) and 32x32 (Subset-2) patches were obtained from 150 CT images to generate sub-datasets. Within the scope of the proposed method, 3000 patch images have been labelled as CoVID-19 and No finding for using in training and testing phase. Feature fusion and ranking method have been applied in order to increase the performance of the proposed method. Then, the processed data was classified with a Support Vector Machine (SVM). According to other pre-trained Convolutional Neural Network (CNN) models used in transfer learning, the proposed method shows high performance on Subset-2 with 98.27% accuracy, 98.93% sensitivity, 97.60% specificity, 97.63% precision, 98.28% F1-score and 96.54% Matthews Correlation Coefficient (MCC) metrics.

COVID-19

Transfer Learning;World Health Organization;Tomography

External

2. Detection/Diagnosis

CT

32968435

10.3892/etm.2020.9210

Yes

PMC7500043

2020-09-25

Journal Article

Peer reviewed (PubMed)

advancing covid-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.

COVID-19;Pneumonia

Exp Ther Med

Art;Transfer Learning;Tomography;Area under Curve

External

2. Detection/Diagnosis

CT

32834641

10.1016/j.chaos.2020.110153

Yes

PMC7381895

2020-08-25

Journal Article

Peer reviewed (PubMed)

automatic distinction between covid-19 and common pneumonia using multi-scale convolutional neural network on chest ct scans

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at .

COVID-19;Pneumonia

Chaos Solitons Fractals

Polymerase Chain Reaction;Reverse Transcription

Self-recorded/clinical

2. Detection/Diagnosis

CT

34127870

10.1016/j.patcog.2021.108109

Yes

PMC8189738

2021-06-16

Journal Article

Peer reviewed (PubMed)

scoat-net: a novel network for segmenting covid-19 lung opacification from ct images

Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.