souravs17031999/Retinal_blindness_detection_Pytorch
AI-driven initiative to assist hospitals and rural clinics in early detection of Diabetic Retinopathy, supporting accessible eye care for all through open healthcare innovation.
# Technical Summary Uses ResNet152 transfer learning in PyTorch to classify retinal fundus images into five DR severity levels (0–4), with a Tkinter GUI frontend and MySQL backend for patient record management. Integrates Twilio SMS notifications for patient outreach and supports the APTOS Kaggle blindness dataset, enabling hospitals to automate DR screening workflows with minimal manual annotation. The architecture prioritizes accessibility for under-resourced clinics while establishing foundations for privacy-preserving deployment through federated learning and differential privacy techniques.
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Oct 06, 2025
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