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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

查看全文 作　　者：Anas [1]Bilal;Azhar [2]Imran;Talha Imtiaz [3,4]Baig;Xiaowen [1]Liu;Haixia [1]Long;Abdulkareem [5]Alzahrani;Muhammad [6]Shafiq 高影响力作者 机构地区：[1]College of Information Science and Technology,Hainan Normal University,Haikou,571158,China;[2]Department of Creative Technologies,Air University,Islamabad,Pakistan;[3]School of Life Science and Technology,University of Electronic Science and Technology of China UESTC,Chengdu,China;[4]School of Science and Technology,University of Management and Technology UMT,Lahore,Punjab,Pakistan;[5]Faculty of Computer Science&Information Technology,Al Baha University,Al Baha,Saudi Arabia;[6]School of Information Engineering,Qujing Normal University,Qujing,China高影响力机构 出　　处：《Computer Systems Science & Engineering》索引2024年第48卷第2期,共18页高影响力期刊 基　　金：This research was funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012);the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536);the Haikou Science and Technology Plan Project of China(No.2022-016);the Project supported by the Education Department of Hainan Province,No.Hnky2021-23. 摘　　要：Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection. 关 键 词：Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification
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