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This project report presents an automated system for detecting and classifying Diabetic Retinopathy (DR) from fundus images using a fine-tuned ResNet-50 deep learning model. The system achieves 87.9% accuracy and an AUC-ROC of 0.962, aiming to enhance early DR screening in resource-limited settings while addressing challenges in manual grading and accessibility. The report outlines the project

Author

sastikadeepika

Language

EN