Accurate classification of diabetic retinopathy (DR) from retinal fundus images is essential for timely intervention, yet challenges like class imbalance, hidden logic, and limited generalization slow down the adoption of deep learning in clinical practice. To address these challenges, we propose REGANXNeta Retinal GAN-augmented eXplainable Network that integrates EfficientNet-B4, InceptionResNetV2, and ResNet101 to utilize their complementary strengths. Class-conditional GAN synthesis augments a 19, 500image dataset from APTOS 2019, EyePACS, DDR, and IDRiD, with rigorous preprocessing including CLAHE and 512 512 normalization to ensure balanced representation and mitigate overfitting. Explainable AI via LIME and SHAP generates heatmaps that highlight specific regions of the lesion such as microaneurysms and hemorrhages, achieving mean IoU scores of 0. 58 (LIME) and 0. 66 (SHAP) against expert masks. Comprehensive evaluation yields 96. 4% accuracy, 96. 9% precision, 96. 1% recall, 96. 5 \% ~F 1 score, and 90. 8% IoU, outperforming Swin Transformer-Tiny (91. 0%), ResNet101 (89. 8%), MobileNetV3Large (88. 9%), EfficientNet-B4 (89. 3%), DenseNet201 (88. 1%), and ConvNeXt-Tiny (87. 2%). Ablation studies confirm synergy from feature fusion, while confusion matrices reveal minimal errors between adjacent DR stages. REGANXNet provides reliable and easy-to-understand DR screening that works on basic computers but needs future improvements to provide faster use on small devices.
K. Silpaja Chandrasekar (Thu,) studied this question.