Diabetic retinopathy (DR) is the major cause of blindness which affects all ages and also around the world. It is hard to diagnose diabetic retinopathy in its initial stage. Diabetes mellitus persistence is the major cause of diabetic retinopathy, which leads to damage of retinal microvasculature and detection of DR is time consuming by the trained experts. Therefore, an automated diagnosis method based on machine learning algorithm is proposed to automatically diagnose the diabetic retinopathy. One of the most common diabetic retinopathy anomalies is exeduate. The yellowish region which varies in size and brightness are characterized as image exeduate. The size of the exeduate depends on stage of patient’s DR disease. This paper describes all those automated systems that have been developed by various computational intelligence and image processing techniques. This paper describes about the limitations of current automated systems and future trends which will help the researchers who are working in this area of research.
Volume 12 | 07-Special Issue
Pages: 240-247
DOI: 10.5373/JARDCS/V12SP7/20202103