Detection of Retinopathy Diabetic Using Explainable AI: Interpretable Deep Learning Models in Clinical Practice

Diabetic Retinopathy (DR) is a significant threat to eyesight and blindness globally, particularly for those with a more extended diabetes history. Deep learning has achieved high accuracy for DR detection using fundus images; however, the "black-box" nature hinders its application in clinical practice, where interpretability is important. In this work, we propose a solution to the model transparency problem by introducing an XAI-enhanced diagnostic framework utilizing CNNs. We present an explainable deep learning framework based on a convolutional neural network (CNN), specifically ResNet-50, which has been fine-tuned on the APTOS 2019 Blindness Detection dataset. To narrow the interpretability gap, we utilize the Grad-CAM and SHAP visualization methods, which generate class-discriminative heatmaps and feature-attribution plots, respectively. The multi-class diabetes retinopathy (DR) classification result yielded an overall accuracy of 83% for the model. Importantly, the explanation agreement score with ophthalmologists is over 78%, indicating a high correlation between the AI-based saliency maps and expert-annotated lesion regions. Our findings show that XAI can not only maintain diagnostic accuracy but also enhance model interpretability, rendering AI-based DR screening systems more acceptable and usable in clinical practice. This study reinforces the importance of explainability as an integral part of implementing medical AI.