A Clinically-Aligned Multi-Family Explainable AI Framework for Diabetic Retinopathy Detection on Fundus Images

Automated diabetic retinopathy (DR) screening has achieved expert-level accuracy, yet clinical adoption remains limited by the opacity of deep neural networks. We address this gap with a DenseNet121-based binary classifier trained on 3,662 retinal fundus images from APTOS 2019, optimised through a two-phase transfer-learning and fine-tuning strategy with focal loss and class-balanced sampling. The model achieves 95.45% test accuracy, an AUC-ROC of 0.9881, sensitivity of 93.91%, and specificity o