Diabetic Retinopathy(DR)is a vision disease due to the long-term prevalenceof Diabetes Mellitus.It affects the retina of the eye and causes severedamage to the vision.If not treated on time it may lead to permanent vi...Diabetic Retinopathy(DR)is a vision disease due to the long-term prevalenceof Diabetes Mellitus.It affects the retina of the eye and causes severedamage to the vision.If not treated on time it may lead to permanent vision lossin diabetic patients.Today’s development in science has no medication to cureDiabetic Retinopathy.However,if diagnosed at an early stage it can be controlledand 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.Inthis paper,we propose an unsupervised clustering technique to automatically clusterthe DR into one of its five development stages.The deep learning based unsupervisedclustering is made to improve itself with the help of fuzzy rough c-meansclustering where cluster centers are updated by fuzzy rough c-means clusteringalgorithm during the forward pass and the deep learning model representationsare updated by Stochastic Gradient Descent during the backward pass of training.The proposed method was implemented using python and the results were takenon DGX server with Tesla V100 GPU cards.An experimental result on the publicallyavailable Kaggle dataset shows an overall accuracy of 88.7%.The proposedmodel improves the accuracy of DR diagnosis compared to the existingunsupervised algorithms like k-means,FCM,auto-encoder,and FRCM withalexnet.展开更多
文摘Diabetic Retinopathy(DR)is a vision disease due to the long-term prevalenceof Diabetes Mellitus.It affects the retina of the eye and causes severedamage to the vision.If not treated on time it may lead to permanent vision lossin diabetic patients.Today’s development in science has no medication to cureDiabetic Retinopathy.However,if diagnosed at an early stage it can be controlledand 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.Inthis paper,we propose an unsupervised clustering technique to automatically clusterthe DR into one of its five development stages.The deep learning based unsupervisedclustering is made to improve itself with the help of fuzzy rough c-meansclustering where cluster centers are updated by fuzzy rough c-means clusteringalgorithm during the forward pass and the deep learning model representationsare updated by Stochastic Gradient Descent during the backward pass of training.The proposed method was implemented using python and the results were takenon DGX server with Tesla V100 GPU cards.An experimental result on the publicallyavailable Kaggle dataset shows an overall accuracy of 88.7%.The proposedmodel improves the accuracy of DR diagnosis compared to the existingunsupervised algorithms like k-means,FCM,auto-encoder,and FRCM withalexnet.
