Diabetic Retinopathy Detection Using Efficient Net and Grad-CAM

Diabetic retinopathy (DR) is a progressive retinal disease and a major cause of preventable blindness among diabetic

patients worldwide. Early diagnosis is essential to avoid irreversible vision loss, yet manual screening methods are often timeconsuming, inconsistent, and inaccessible in low-resource settings. This project presents an AI-based system for automated DR

detection and severity grading using deep learning techniques applied to retinal fundus images from APTOS, Messidor, and

EyePACS datasets. The model classifies DR into five clinically defined stages—ranging from No DR to Proliferative DR—by

learning subtle pathological features such as microaneurysms, hemorrhages, and neovascularization. To enhance interpretability

and clinical trust, Grad-CAM is integrated to generate heatmaps that highlight lesion-specific regions influencing the model's

predictions. The system achieves 84% test accuracy with F1-scores of 0.93 for No DR and 0.90 for Proliferative DR. The system

is optimized for deployment on CPU-based hardware, making it suitable for scalable and accessible screening in real-world

environments. By combining diagnostic accuracy with explainability, this project demonstrates how AI can support

ophthalmologists, improve early intervention, and reduce the global burden of DR-related vision impairment.