Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

作     者：S.Sudha A.Srinivasan T.Gayathri Devi 

作者机构：Department of ECESRCSASTRA Deemed UniversityKumbakonam612001TamilnaduIndia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第45卷第5期

页      面：1985-2000页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：CNN networking segmentation hybrid classifier data set crossvalidation fundus image 

摘      要：The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the *** feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent ***,this type of disorder can be only screened and identified through the processing of fundus *** different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of *** the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion *** CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)*** this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.