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Early Detection of Diabetic Retinopathy Using EfficientNet-Based Convolutional Neural Networks

Diabetic retinopathy is a diabetes-related eye disease and a major cause of preventable blindness, especially in regions with limited access to ophthalmologists. This research developed an EfficientNet-B0-based convolutional neural network to detect and classify diabetic retinopathy into five severity levels: No DR, Mild, Moderate, Severe, and Proliferative. The system used a balanced dataset of 10,000 retinal images, with preprocessing techniques including CLAHE, resizing, and data augmentation to improve image quality and model robustness. Transfer learning and attention-based feature extraction were used to improve diagnostic sensitivity. The model achieved 94.97% accuracy, 94.84% sensitivity, and 95.1% specificity. It was integrated into a Flask-based web application with Grad-CAM visualization, providing interpretable model outputs for physicians and supporting scalable, cost-effective screening in resource-limited healthcare settings.