Deep Learning for Diabetic Retinopathy Detection and Classification Based on Fundus Images

Diabetic Retinopathy (DR) is a serious complication caused by diabetes that affects the retina, leading to potential blindness if not treated early. This paper explores how deep learning models can be used to automatically detect and classify DR from retinal fundus images. The approach uses various con- volutionalneural networks (CNNs) including VGGNet, ResNet, and DenseNet, trained on large public datasets like Kaggle and MESSIDOR. The aim is to develop a reliable, efficient, and accurate system that supports early detection and clinical screening of DR, reducing the global burden of vision loss.