Exudate Detection in Fundus Images Using Deep Learning Algorithms

Abstract

Diabetic Retinopathy (DR) affects people who have diabetes mellitus for a long period (20 years). It is one of the most common causes of preventable blindness in the world. If not detected early, this may cause irreversible damage to the patient's vision. One of the signs and serious DR anomalies are exudates, so these lesions must be properly detected and treated as soon as possible. To address this problem, the authors propose a novel method that focuses on the detection and classification of Exudateas Hard and soft in retinal fundus images using deep learning. Initially, the authors collected the retinal fundus images from the IDRID dataset, and after labeling the exudate with the annotation tool, the YOLOV3 is trained with specific parameters according to the classes. Then the custom detector detects the exudate and classifies it into hard and soft exudate.