Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm  

作　　者：Shao-Jun Zhu Hao-Dong Zhan Mao-Nian Wu Bo Zheng Bang-Quan Liu Shao-Chong Zhang Wei-Hua Yang 

机构地区：[1]Huzhou University,School of Information Engineering,Huzhou 313000,Zhejiang Province,China [2]Zhejiang Province Key Laboratory of Smart Management&Application of Modern Agricultural Resources,Huzhou University,Huzhou 313000,Zhejiang Province,China [3]College of Digital Technology and Engineering,Ningbo University of Finance&Economics,Ningbo 315175,Zhejiang Province,China [4]Shenzhen Eye Institute,Shenzhen Eye Hospital,Jinan University,Shenzhen 518048,Guangdong Province,China

出　　处：《International Journal of Ophthalmology(English edition)》2023年第7期995-1004,共10页国际眼科杂志（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.61906066);the Zhejiang Provincial Philosophy and Social Science Planning Project(No.21NDJC021Z);Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014);Sanming Project of Medicine in Shenzhen(No.SZSM202011015);Shenzhen Science and Technology Planning Project(No.KCXFZ20211020163813019);the Natural Science Foundation of Ningbo City(No.202003N4072);the Postgraduate Research and Innovation Project of Huzhou University(No.2023KYCX52)。

摘　　要：AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effe

关 键 词：high myopic maculopathy deep learning active learning image classification ALFA-Mix algorithm 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R778.11[自动化与计算机技术—计算机科学与技术]