Improving Diabetic Retinopathy Detection by using Image Processing and Machine Learning

The rapid development and proliferation of medical imaging technologies is revolutionizing medicine. Medical

imaging allows scientists and physicians to glean potentially life-saving information by peering noninvasively into the human

body. Diabetic retinopathy (DR) is an irreversible fundus retinopathy. A deep learning-based auto-mated DR diagnosis system

can save diagnostic time. This paper reviews and analyses state-of-the-art deep learning methods in supervised, self-supervised

and Vision Transformer setups, proposing retinal fundus image classification and detection. For instance, referable, nonreferable and proliferative classifications of Diabetic Retinopathy are reviewed and summarized. Moreover, this paper discusses

the available retinal fundus datasets for Diabetic Retinopathy that are used for tasks such as detection, classification and

segmentation. The paper also assesses research gaps in the area of DR detection/classification and addresses various challenges

that need further study and investigation