Explainable DL Models for Automated Ophthalmic Diagnosis
Anas Awan
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Posted Nov 8, 2024
Developed explainable CNN models for retinal OCT scan classification, achieving high accuracy and providing insights via XAI techniques like Grad-Cam and LIME.
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Shifa International Hospitals
Quaid-e-Azam International Hospital
Developed a comprehensive machine learning solution for automating diagnostic processes in ophthalmology using CNN architectures. This project includes the full machine learning lifecycle: data preprocessing, model architecture design, training, evaluation, and explainability. The DA-CNN and CF-CNN models were designed with enhanced feature extraction and explainability, achieving 97.4% and 96.68% accuracy respectively on retinal OCT scans for conditions such as DME, Drusen, and CNV. The models also integrate interpretability techniques like GradCam and LIME, providing visual explanations for diagnostic decisions.