Early Detection of Diabetic Retinopathy using Machine Learning Classifiers

Abstract

Diabetic Retinopathy (DR) is a serious complication of diabetes that can lead to blind ness if not detected early. Diabetes, along with diabetic retinopathy, is associated with causing harm to the eyes, characterized by the leakage of blood vessels damaging the retina, leading to microaneurysms (MA), hard exudates (HE), and soft exudates (cot ton wool spots). As a chronic condition, DR poses a growing challenge worldwide, often resulting in visual impairment or blindness if left undiagnosed. Early detection is crucial, yet challenging, as symptoms manifest only after signi cant retinal changes occur. This study investigates the e ectiveness of a multimodal approach using ma chine learning classi ers for the early detection of DR, employing both binary and multiclass classi cation methods. We utilized K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and ResNet-50 algorithms to classify retinal images. In binary classi cation, designed to distinguish between DR and non-DR images, KNN achieved an accuracy of 88%, SVM reached 92%, and ResNet-50 outperformed both with an accuracy of 98%. These results demonstrate the potential of machine learning, par ticularly deep learning with ResNet-50, in accurately identifying the presence of DR. For multiclass classi cation, which aims to classify images into di erent stages of DR (no DR, mild, moderate, severe, and proliferative DR), KNN achieved an accuracy of 70%, SVM achieved 71%, and ResNet-50 achieved the highest accuracy of 82%. The study's ndings highlight the superior performance of ResNet-50 in both binary and multiclass classi cation tasks, suggesting its suitability for automated DR screening systems. The implementation of such advanced machine learning models can aid in early diagnosis, thus facilitating timely treatment and potentially preventing vision loss in diabetic patients.