An Explainable Hybrid Deep Learning Framework for Retinal Disease Classification Using DenseNet201 and SHAP-Optimized xLSTM

Retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and cataracts are among the leading causes of blindness and visual impairment worldwide. These conditions often progress silently without noticeable symptoms during the early stages, making timely and accurate diagnosis crucial to prevent irreversible vision loss. Retinal fundus imaging provides a non-invasive approach for capturing structural abnormalities in the retina and plays a vital role in automated disease screening. In this study, we propose a multi-class retinal disease classification framework using fundus images. The proposed methodology integrates deep feature extraction using DenseNet201, feature selection using SHAP to identify the most informative features, and an xLSTM classifier for final prediction. Experiments were conducted on the AMDNet23 dataset, which contains 2,000 curated fundus images across four balanced classes: Normal, Diabetes, Cataract, and Age-related Macular Degeneration. The proposed configuration achieved an accuracy of 92.00%, an AUC of 0.9893, and an MCC of 0.8958, outperforming other evaluated model combinations. These results demonstrate the effectiveness of combining deep feature learning with interpretable feature selection for retinal disease detection and highlight the potential of the proposed framework as a reliable clinical decision-support tool for automated retinal screening.