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.
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