Detecting Exudates in Color Fundus Images for Diabetic Retinopathy Detection Using Deep Learning

Abstract

From a medical point of view, diabetes is believed to be the foundation of several other health problems and late complications. The increase in diabetes-related disorders has posed a challenge to the healthcare sector. Diabetes can cause a variety of issues such as diabetic neuropathy, diabetic nephropathy and diabetic retinopathy. The most common complication due to diabetes is Diabetic Retinopathy (DR). The signs of Diabetic Retinopathy include red lesions, bright lesions and neovascularization. Bright lesions are second clinically observable lesions which occurs after red lesions. It includes cotton wool spots and hard exudates (soft and hard exudates). It occurs during the severe stage of the DR disease and leads to severe vision loss if not treated properly. The proposed work's primary objective is to use deep learning architecture to construct an automated model for detecting bright lesions for non-proliferative stage diabetic retinopathy screening. Several pre-processing stages are followed by removing the background of the images, optic disc (OD) elimination and candidate lesion segmentation are done. The model was trained and tested using MESSIDOR datasets and achieved the maximum accuracy, sensitivity and specificity of 97.54%, 90.34% and 98.24% respectively.