Open Access

ARTICLE

Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

R. S. Rajkumar^*, A. Grace Selvarani

Sri Ramakrishna Engineering College, Coimbatore, 641022, India

* Corresponding Author: R. S. Rajkumar. Email:

Computer Systems Science and Engineering 2022, 42(2), 509-521. https://doi.org/10.32604/csse.2022.021909

Received 19 July 2021; Accepted 03 September 2021; Issue published 04 January 2022

Abstract

Diabetic Retinopathy (DR) is a vision disease due to the long-term prevalence of Diabetes Mellitus. It affects the retina of the eye and causes severe damage to the vision. If not treated on time it may lead to permanent vision loss in diabetic patients. Today’s development in science has no medication to cure Diabetic Retinopathy. However, if diagnosed at an early stage it can be controlled and permanent vision loss can be avoided. Compared to the diabetic population, experts to diagnose Diabetic Retinopathy are very less in particular to local areas. Hence an automatic computer-aided diagnosis for DR detection is necessary. In this paper, we propose an unsupervised clustering technique to automatically cluster the DR into one of its five development stages. The deep learning based unsupervised clustering is made to improve itself with the help of fuzzy rough c-means clustering where cluster centers are updated by fuzzy rough c-means clustering algorithm during the forward pass and the deep learning model representations are updated by Stochastic Gradient Descent during the backward pass of training. The proposed method was implemented using python and the results were taken on DGX server with Tesla V100 GPU cards. An experimental result on the publically available Kaggle dataset shows an overall accuracy of 88.7%. The proposed model improves the accuracy of DR diagnosis compared to the existing unsupervised algorithms like k-means, FCM, auto-encoder, and FRCM with alexnet.

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