An explainable deep learning framework for multi-disease retinal diagnosis

Automated diagnosis of retinal diseases from fundus images has become an important application of deep learning (DL) for supporting timely clinical diagnosis and treatment. Although existing DL models have achieved high predictive accuracy, many offer limited interpretability, lack rigorous validation of their explanations, and do not integrate ensemble learning with explainable artificial intelligence (XAI) approaches, thereby reducing their clinical utility. This paper proposes XDR-Net, an explainable deep learning framework for multi-disease retinal diagnosis that integrates accurate prediction with disease-specific and quantitatively evaluated explanations. The framework makes three main contributions. First, it combines four complementary convolutional neural networks (CNNs), VGG19, DenseNet121, MobileNetV2, and AlexNet, into a heterogeneous ensemble to improve classification performance on the evaluated dataset. Second, it introduces a disease-aware explanation strategy that selects the most appropriate explanation method according to disease characteristics, using Gradient Weighted Class Activation Mapping Plus Plus (Grad-CAM++) for structural abnormalities and patch-based Multiple Instance Learning (MIL) for lesion-centered diseases, thereby producing disease-specific model explanations. Third, it incorporates a quantitative validation module that evaluates explanation reliability using the confidence drop and energy concentration metrics to quantitatively assess model prediction sensitivity to highlighted image regions. Experiments on fundus image dataset achieved a classification accuracy of 94.02% across diabetic retinopathy, glaucoma, cataract, and normal cases. Comparative evaluations with the evaluated baseline approaches showed competitive predictive performance and quantitative analysis of the generated explanations. These findings indicate that XDR-Net provides competitive classification performance and disease-specific model explanations on the evaluated dataset.