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

BackgroundIn 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.MethodsWith 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.ResultsWe 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.ConclusionsThe results indicate that the suggested model distinguishes all phases of DR and performs better than existing models in terms of accuracy and implementation time.

Automated summary

This article presents a computerized system for the analysis and assessment of diabetic retinopathy (DR) based on retinal fundus photographs. The researchers utilized the deep learning model YOLO V3 to recognize and classify DR. The results indicate that the suggested model performs better than existing models in terms of accuracy and implementation time.