Stage-Wise Categorization and Prediction of Diabetic Retinopathy Using Ensemble Learning and 2D-CNN

作     者：N.M.Balamurugan K.Maithili T.K.S.Rathish Babu M.Adimoolam 

作者机构：Department of Computer Science and EngineeringSri Venkateswara College of EngineeringSriperumbudurChennaiIndia Department of Computer Science and EngineeringKG Reddy College of Engineering and Technology(Autonomous)HyderabadTelanganaIndia Department of Computer Science and EngineeringSridevi Women’s Engineering CollegeHyderabadTelanganaIndia Department of Computer Science and EngineeringSaveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalamChennaiIndia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第4期

页      面：499-514页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 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）] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Diabetic retinopathy prediction and classification ensemble learning conventional neural network diabetic eye disease 

摘      要：Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical *** techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The Machine Learning(ML)and the Deep Learning(DL)algorithms are the predomi-nant techniques to project and explore the images of *** though some solu-tions were adapted to challenge the cause of DR disease,still there should be an efficient and accurate DR prediction to be adapted to refine its *** this work,a hybrid technique was proposed for classification and prediction of *** proposed hybrid technique consists of Ensemble Learning(EL),2 Dimensional-Conventional Neural Network(2D-CNN),Transfer Learning(TL)and Correlation ***,the Stochastic Gradient Boosting(SGB)EL method was used to predict the ***,the boosting based EL method was used to predict the DR of *** 2D-CNN was applied to categorize the various stages of DR ***,the TL was adopted to transfer the clas-sification prediction to training *** this TL was applied,a new predic-tion feature was *** the experiment,the proposed technique has achieved 97.8%of accuracy in prophecies of DR images and 98%accuracy in grading of *** experiment was also extended to measure the sensitivity(99.6%)and specificity(97.3%)*** predicted accuracy rate was com-pared with existing methods.