An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images

Background In this article, we present a computerized system for the analysis and assessment of diabetic retinopathy (DR) based on retinal fundus photographs. DR is a chronic ophthalmic disease and a major reason for blindness in people with diabetes. Consistent examination and prompt diagnosis are the vital approaches to control DR. Methods With the aim of enhancing the reliability of DR diagnosis, we utilized the deep learning model called You Only Look Once V3 (YOLO V3) to recognize and classify DR from retinal images. The DR was classified into five major stages: normal, mild, moderate, severe, and proliferative. We evaluated the performance of the YOLO V3 algorithm based on color fundus images. Results We have achieved high precision and sensitivity on the train and test data for the DR classification and mean average precision (mAP) is calculated on DR lesion detection. Conclusions The results indicate that the suggested model distinguishes all phases of DR and performs better than existing models in terms of accuracy and implementation time.