A Robust Intelligent CNN Model Enhanced with Gabor-Based Feature Extraction, SMOTE Balancing, and Adam Optimization for Multi-Grade Diabetic Retinopathy Classification

Diabetic retinopathy (DR) is a leading cause of vision impairment and permanent blindness worldwide, requiring an accurate and automated system to classify its multi-grade severity to ensure timely patient intervention. However, standard Convolutional Neural Networks (CNNs) often struggle to capture the fine, high-frequency microvascular patterns critical for diagnosis. This study proposes a Robust Intelligent CNN Model (RICNN) designed to improve multi-level DR classification by integrating Gabor-based feature extraction with deep learning. The model also incorporates SMOTE (Synthetic Minority Oversampling Technique) balancing and Adam optimization for efficient convergence. The proposed RICNN was evaluated on the Messidor dataset (1,200 images) across four severity levels: Mild, Moderate, Severe, and Proliferative DR. The results showed that RICNN achieved superior performance with 89% accuracy, 88.75% precision, 89% recall, and 89% F1-score. The model also demonstrated high robustness in identifying advanced stages, achieving AUCs of 97% for Severe DR and 99% for Proliferative DR. Comparative analysis confirms that texture-aware Gabor enhancement significantly outperforms Local Binary Pattern (LBP) and Color Histogram approaches. These findings indicate that the proposed RICNN provides a reliable and intelligent foundation for clinical decision support systems, potentially reducing diagnostic errors and preventing vision loss in high-risk populations.