Efficient retinal exudates detection method using ELNet in diabetic retinopathy assessment

Diabetic Retinopathy (DR) can be detected at earlier stage by detecting exudates in retinal fundus images. In this article, the exudates are detected and segmented using the proposed Enhanced LeNet (ELNet) classification method. The proposed exudates segmentation method consists of the following modules Retinal image classification and Exudates segmentation. In retinal image classification, the retinal images are data augmented and then ELNet classification architecture is used to classify the retinal image into either normal or abnormal. In exudates segmentation module, the exudates are detected and segmented using Kirsch edge detector. The performance of the exudate’s detection method is improved by detecting and eliminating the blood vessels in the retinal image before detecting the exudates. In this paper, Digital Retinal Images for Vessel Extraction (DRIVE) and Diabetic Retinopathy database (DIARETDB1) retinal image datasets are used for the detection of exudates in the retinal images. In this study, the proposed method showcases remarkable results, demonstrating a sensitivity of 99.31% and 99.31%, specificity of 97.44% and 95%, and an accuracy of 99.09% and 98.8% for the DRIVE and DIARETDB1 datasets, respectively. Exudates detection in both datasets without eliminating OD and retinal blood vessels, we observe similar accuracy rates, Average 96.5% for both datasets. However, when eliminating OD and retinal blood vessels, the accuracy significantly improved for both datasets, reaching approximately 99.2% average. The performance is analyzed and compared with other state of the art methods.