Explainable Multi-Classification of Retinal Diseases Using Ensembled Transfer Learning Models and Grad-CAM

Abstract

Retinal diseases are a leading cause of vision impairment globally, necessitating early and accurate diagnosis. This study proposes an explainable multiclassification system for retinal fundus images using ensembled transfer learning models and Gradient-weighted Class Activation Mapping (Grad-CAM). Retinal images were preprocessed using a pipeline comprising Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological erosion, and bilateral filtering. Data augmentation through random transformations was applied for class balance and model robustness. Four pretrained architectures—ResNet50, VGG19, EfficientNetB5, and DenseNet201—were evaluated in both baseline and Bayesianoptimized configurations. Performance was assessed via five-fold cross-validation using sensitivity, specificity, accuracy, F1-score, and ROC-AUC metrics. The top three models based on ROC-AUC (EfficientNetB5: 97.36%, EfficientNetB5 Optimized: 96.96%, DenseNet201 Optimized: 96.40%) were ensembled. Among ensemble strategies, soft voting outperformed hard voting, achieving the highest test accuracy of 88.00% and a macro F1-score of 87.85%. Grad-CAM visualizations provided class-specific interpretability by highlighting pathological regions within fundus images. A Streamlit-based graphical user interface was developed, enabling users to upload retinal images and receive real-time classifications, class probability scores, and Grad-CAM heatmaps. The proposed system demonstrates strong potential as a clinical decision-support tool and educational platform, combining high classification performance with visual interpretability.