Hybrid deep learning models for diabetic retinopathy stage classification using fundus images

Diabetic retinopathy (DR) is a leading cause of vision loss in individuals with diabetes, making early and accurate detection essential for preventing severe complications. Automated classification of DR stages from retinal fundus images can assist clinicians in timely diagnosis and management. This study proposes a hybrid approach that combines deep learning-based feature extraction with traditional machine learning classifiers for automatic DR stage classification. Two datasets were used: Dataset A (DiabeticRetinopathy_Messidor_EyePACS_Preprocessed) and Dataset B (APTOS 2019 Blindness Detection). Three hybrid architectures were evaluated: MobileNetV2 with Support Vector Machine (SVM), MobileNetV2 with Random Forest (RF), and VGG16 with SVM. The models exploit convolutional neural networks for extracting discriminative features and employ conventional classifiers for robust decision-making. Grad-CAM and Score-CAM techniques were applied to enhance interpretability by visualizing the regions influencing model predictions. Experimental results on Dataset B show that MobileNetV2 + SVM achieved an accuracy of 85%, precision of 72%, recall of 75%, and F1 score of 73%, while MobileNetV2 + RF achieved an accuracy of 85%, precision of 74%, recall of 72%, and F1 score of 73%. These results indicate that lightweight CNNs combined with traditional classifiers can produce reliable and interpretable DR stage predictions. The study highlights the potential of hybrid models for clinical deployment, offering accurate, transparent, and efficient tools to support ophthalmologists in DR screening and management. Future work will focus on addressing data imbalance, improving model generalizability, and integrating these methods into real-world clinical diagnostic systems.

The online version contains supplementary material available at 10.1007/s40200-026-01938-z.