Efficient Detection of Diabetic Retinopathy Through Deep Learning

This research paper and presented an efficient hybrid framework for diabetic retinopathy severity classification using retinal fundus images. The proposed system integrates preprocessing, augmentation, GoogleNet and ResNet feature extraction, hybrid feature fusion, APSO feature selection, and machine learning classification. The experimental results reported in the thesis show that Random Forest performed best on APSO-optimized hybrid features, achieving 94% accuracy, 93% precision, 92% recall, and 93% F1-score. Class-wise results and confusion matrix analysis indicate strong discrimination across No DR, Mild DR, Moderate DR, Severe DR, and PDR classes, with most errors occurring between neighboring severity levels. The study demonstrates that combining deep learning representation learning with evolutionary optimization and classical machine learning can improve automated DR screening performance.

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