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diabetic retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering

Computer Systems Science & Engineering 2022年第8期42卷 509-521页

作者： R.S.Rajkumar A.Grace Selvarani Sri Ramakrishna Engineering College Coimbatore641022India

diabetic retinopathy(DR)is a vision disease due to the long-term prevalenceof Diabetes *** affects the retina of the eye and causes severedamage to the *** not treated on time it may lead to permanent vision lossin diabetic ***’s development in science has no medication to curediabetic ***,if diagnosed at an early stage it can be controlledand permanent vision loss can be *** to the diabetic population,experts to diagnose diabetic retinopathy are very less in particular to local *** an automatic computer-aided diagnosis for DR detection is *** paper,we propose an unsupervised clustering technique to automatically clusterthe DR into one of its five development *** 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 *** proposed method was implemented using python and the results were takenon DGX server with Tesla V100 GPU *** 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.

关键词： diabetic retinopathy detection diabetic retinopathy diagnosis fuzzy rough c-means clustering unsupervised CNN clustering

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Intelligent Prediction Approach for diabetic retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

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Computers, Materials & Continua 2021年第2期66卷 1613-1629页

作者： G.Arun Sampaul Thomas Y.Harold Robinson E.Golden Julie Vimal Shanmuganathan Seungmin Rho Yunyoung Nam CSE Department J.B.Institute of Engineering and TechnologyHyderabad500075India School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Anna University Regional CampusTirunelveli627007India Department of Information Technology National Engineering CollegeKovilpatti628503India Department of Software Sejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision *** of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked *** this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate *** CNN model is trained by different images of eyes that have retinopathy and those which do not have *** fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent *** feature loss factor increases the label value to identify the patterns with the kernel-based *** performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and *** results show that the proposed CNN performs better.

关键词： Convolutional neural networks dental diagnosis image recognition diabetic retinopathy detection

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Computer-aided diabetic retinopathy diagnostic model using optimal thresholding merged with neural network

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International Journal of Intelligent Computing and Cybernetics 2020年第3期13卷 283-310页

作者： Ambaji S.Jadhav Pushpa B.Patil Sunil Biradar Department of Electrical and Electronics B.L.D.E.A’s V.P.Dr.P.G.Halakatti College of Engineering and Technology(Affiliated to Visvesvaraya Technological University)VijayapurIndia Department of Ophthalmology Shri B.M.Patil Medical College and Research Center(Deemed to be University)VijayapurIndia

Purpose-diabetic retinopathy(DR)is a central root of blindness all over the *** DR is tough to diagnose in starting stages,and the detection procedure might be time-consuming even for qualified ***,intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various ***,a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark ***/methodology/approach-The proposed DR diagnostic procedure involves four main steps:(1)image pre-processing,(2)blood vessel segmentation,(3)feature extraction,and(4)***,the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization(CLAHE)and average *** the next step,the blood vessel segmentation is carried out using a segmentation process with optimized gray-level *** the blood vessels are extracted,feature extraction is done,using Local Binary Pattern(LBP),Texture Energy Measurement(TEM based on Laws of Texture Energy),and two entropy computations-Shanon’s entropy,and Kapur’s *** collected features are subjected to a classifier called Neural Network(NN)with an optimized training *** the gray-level thresholding and NN is enhanced by the Modified Levy Updated-Dragonfly Algorithm(MLU-DA),which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the ***,this classification error can correctly prove the efficiency of the proposed DR detection ***-The overall accuracy of the proposed MLU-DA was 16.6%superior to conventional classifiers,and the precision of the developed MLU-DA was 22%better than LM-NN,16.6%better than PSO-NN,GWO-NN,and ***,it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting ***

关键词： diabetic retinopathy detection Gray-level thresholding Optimal trained neural network Dragon fly algorithm Levy update Performance metrics

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