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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- medical |
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- covid |
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- covid19 |
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- xray |
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--- |
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# COVID-19 Detection using VGG19 and X-ray Images |
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## Overview |
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This model is able detect COVID-19 from X-ray images using the VGG19 architecture for transfer learning. The dataset used for this project is the COVID-19 Radiography Database available on Kaggle. |
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## Dataset |
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The dataset used in this project is the [COVID-19 Radiography Database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database). It contains X-ray images categorized into three classes: COVID, Normal, and other pneumonia. The dataset is split into training, validation, and test sets to ensure robust evaluation of the model. |
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## Methodology |
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### 1. Import Libraries |
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We start by importing the necessary libraries required for data processing, model building, and evaluation. These include TensorFlow for deep learning, matplotlib for visualization, and other essential packages. |
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### 2. Load Dataset |
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The dataset is loaded from the specified directory. This dataset contains X-ray images categorized into COVID, Normal, and other pneumonia classes. The images are stored in respective folders, which are read and preprocessed. |
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### 3. Data Preprocessing |
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- **Data Augmentation:** To increase the diversity of our training data, various transformations such as rotation, zoom, and horizontal flip are applied. This helps in making the model robust and prevents overfitting. |
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- **Rescaling:** The pixel values are rescaled to the range [0, 1] to standardize the input data, which improves model performance. |
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### 4. Split Dataset |
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The dataset is split into training, validation, and test sets. This is crucial for evaluating the model's performance on unseen data. |
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- **Training Set:** Used to train the model. |
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- **Validation Set:** Used to tune hyperparameters and prevent overfitting. |
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- **Test Set:** Used to assess the final model's performance. |
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### 5. Build the Model using VGG19 |
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- **Transfer Learning:** The pre-trained VGG19 model, which has been trained on a large dataset (ImageNet), is used to leverage the learned features from a different domain to our specific task of COVID-19 detection. |
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- **Model Architecture:** Custom layers are added on top of VGG19 to adapt it to our classification problem. This includes flattening the output, adding dense layers, and a final softmax layer for classification. |
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### 6. Compile the Model |
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- **Loss Function:** 'binary_crossentropy' is used as the loss function because we have more than two classes. |
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- **Optimizer:** The Adam optimizer is used to adjust the learning rate dynamically. |
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- **Metrics:** Accuracy is tracked to monitor the performance of the model. |
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### 7. Train the Model |
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- **Epochs:** The number of times the entire training dataset is passed forward and backward through the neural network. |
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- **Batch Size:** The number of training examples utilized in one iteration. |
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- **Validation Data:** Helps in monitoring the model's performance on unseen data during training to tune hyperparameters and avoid overfitting. |
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### 8. Evaluate the Model |
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The model is evaluated on the test set to determine its accuracy, precision, recall, and F1 score. This helps in understanding the model's performance comprehensively. |
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### 9. Visualize Training Results |
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- **Loss and Accuracy Plots:** Visualize the training and validation loss and accuracy to understand how well the model is learning and if it's overfitting or underfitting. |
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- **Confusion Matrix:** Provides a detailed breakdown of true positives, false positives, true negatives, and false negatives, giving insights into where the model is making errors. |
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### 10. Conclusion |
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The findings and the performance of the model are summarized. Potential improvements or future work such as experimenting with different architectures, more data, or advanced preprocessing techniques are discussed. |
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## Results |
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The model achieves an accuracy of 98.1% on the test set, indicating its effectiveness in detecting COVID-19 from X-ray images. The high accuracy demonstrates the successful application of data preprocessing, augmentation, and model training techniques. |
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## Acknowledgements |
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- [COVID-19 Radiography Database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database) |
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- [VGG19 Model](https://arxiv.org/abs/1409.1556) |
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