[Code Addition Request]: COVID Detection from CXR Using Explainable CNN

[inkerton]

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Latest Merged PR Link

#577

Project Description

This project aims to develop a robust and explainable Convolutional Neural Network (CNN) model to accurately detect COVID-19 infections from Chest X-ray (CXR) images. By leveraging the power of deep learning and explainable AI techniques, this model will not only provide accurate predictions but also offer insights into the decision-making process, enhancing trust and transparency in medical diagnosis.

Key Objectives:

1. Accurate COVID-19 Detection: Develop a highly accurate CNN model capable of differentiating between COVID-19 positive and negative CXR images.
2. Explainable AI: Implement techniques to visualize and interpret the model's decision-making process, providing insights into the features that contribute to the classification.
3. Robustness and Generalizability: Ensure the model's robustness by training it on a diverse dataset and evaluating its performance on unseen data.
4. User-Friendly Interface: Create a user-friendly interface for medical professionals to easily input CXR images and receive accurate predictions with explanations.

Methodology:

1. Data Acquisition and Preprocessing:

   • Collect a large and diverse dataset of CXR images, including both COVID-19 positive and negative cases.
   • Preprocess the images to ensure consistency in size, format, and intensity levels.
   • Augment the dataset using techniques like rotation, flipping, and noise addition to improve the model's generalization ability.

2. Model Architecture:

   • Design a deep CNN architecture, such as VGG16 or ResNet, to extract relevant features from the CXR images.
   • Incorporate attention mechanisms or other explainable AI techniques to highlight the regions of interest in the images that influence the model's predictions.

3. Training and Optimization:

   • Train the model using an appropriate loss function (e.g., categorical cross-entropy) and optimizer (e.g., Adam).
   • Implement techniques like early stopping and learning rate reduction to prevent overfitting and improve convergence.

4. Evaluation and Validation:

   • Evaluate the model's performance using metrics like accuracy, precision, recall, F1-score, and AUC-ROC curve.
   • Conduct cross-validation to assess the model's generalization ability on different data splits.

5. Explainability Techniques:

   • Employ techniques like Grad-CAM, SHAP, or LIME to visualize the model's decision-making process and identify the most influential features.
   • Generate heatmaps to highlight the regions of the CXR image that contribute most to the classification.

Full Name

inkerton

Participant Role

GSSOC

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