Developing a Transparent Diagnosis Model for Diabetic Retinopathy Using Explainable AI

DOI

https://www.doi.org/10.59256/ijire.20250606019

Published In

Volume 6, Issue 6 (2025)

Published Online

November-December 2025

Pages

124-129

Article Type

Original Article

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Diabetic retinopathy (DR) is a major global health issue, accurate and timely mass screening to prevent vision loss. Although deep learning (DL) models have shown expert-level performance in automatically grading diabetic retinopathy (DR) from fundus images, their inherent "black box" nature presents a significant barrier to their use in clinical settings. Clinicians need diagnostic explanations to ensure patient safety, confirm the model's reliability, and maintain accountability. This is a requirement that systems lacking transparency cannot fulfill. This study presents the creation and testing of a Transparent Diagnosis Model (TDM) for Diabetic Retinopathy, using Explainable Artificial Intelligence (XAI) methods. Our approach combines a customized Convolutional Neural Network (CNN) for classification with the Grad-CAM method to create visual explanations at the same time.