A robust ensemble-based deep learning framework for automated retinal disease detection

ObjectiveTo develop a robust deep learning framework for automated multi-class retinal disease detection supporting clinical decision-making, addressing existing models' limitations in generalizability and accuracy.MethodsA novel ensemble model, ResEfficientNetB3, integrating EfficientNetB3 and ResNet50, was proposed. Two Kaggle datasets were used: Dataset 1 (4217 images, four classes) and Dataset 2 (8230 images, eight classes). Images were resized to 224 × 224 with augmentation (rotation ±20°, zoom 0.8-1.2, flipping, scaling). Models were trained using the Adam optimizer (learning rate = 1e-4, batch size = 20) for up to 50 epochs with early stopping and dropout (0.3-0.5). Performance was assessed via standard splits, five-fold cross-validation, and cross-dataset validation.ResultsResEfficientNetB3 achieved 99.0% accuracy on Dataset 1 and 98.2% on Dataset 2, outperforming EfficientNetB3 (94.0%) and ResNet50 (91.0%). Five-fold validation confirmed robustness (99.0% ± 0.2 and 98.2% ± 0.3), and cross-dataset validation showed strong transferability (94.5-95.8%).ConclusionResEfficientNetB3 effectively combines EfficientNetB3's scaling and ResNet50's residual learning, demonstrating superior accuracy, robustness, and generalization. The model offers a reliable, clinically applicable tool for automated retinal disease detection in real-world diagnostics.