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Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

Computers, Materials & Continua 2022年第7期72卷 597-609页

作者： S.Sudha A.Srinivasan T.Gayathri Devi Department of ECE SRCSASTRA Deemed UniversityKumbakonamIndia

Diabetes is associated with many complications that could lead to *** retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision *** identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for *** clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be *** research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic ***,we aimed to develop a novel integrated approach to increase the accuracy of *** approach utilized both convolutional neural networks and signal processing *** this proposed method,the biological electro retinogram(erg)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify *** the BSN system,electrodes were used to record ergsignal,which was preprocessed to be *** was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were *** neural network(ANN)classifier was used to classify the signals of eyes with DR and normal ***,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based *** DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR ***,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated *** accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,***

关键词： Deep convolution neural network artificial neural network nonproliferative diabetic retinopathy biological erg sensor network

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