DR-XAI: Explainable Deep Learning Model for Accurate Diabetic Retinopathy Severity Assessment

Abstract

To avoid irreversible vision loss, early detection and diagnosis of Diabetic Retinopathy (DR) severity is critical. The percentage of people undertaking eye examinations has risen in recent years, increasing the burden on Ophthalmologists. Various Artificial Intelligence (AI) screening systems have recently been deployed to improve the accuracy of DR diagnosis. However, owing to their black-box nature, most successful AI screening systems are still held back in reality for medical decision aid. The need for an Explainable Artificial Intelligence (XAI) screening system to help Ophthalmologists in DR diagnosis is inevitable. The proposed work is divided into three phases: (i) pre-processing, (ii) optic disk localization, and (iii) DR severity classification. On top of the deep learning model, the proposed work implements a Local Interpretable Model-agnostic Explanations (LIME) explainer to describe what features of the retinal image took part in justifying the predictions. The proposed framework outputs a pixel-value tensor, explaining the possible pixel values contributing to the model’s prediction. MESSIDOR data collection is used for experimental analysis. When compared with other deep learning models, the proposed framework achieved a better accuracy of 98.04%, sensitivity of 99.69%, specificity of 96.37%, f1-score of 96.99% and error rate of 3.60%. Incorporating explainable deep learning models for diabetic retinopathy severity grading improves diagnostic accuracy and provides clinicians with clear insights, enabling trust and informed decision-making in DR diagnosis. This proposed technique enormously advances more effective and responsible healthcare procedures.