计算机与现代化Issue(2)：81-87,126,8.DOI:10.3969/j.issn.1006-2475.2024.02.013
基于对比学习MocoV2的COVID-19图像分类
Image Classification of COVID-19 Based on Contrast Learning MocoV2
摘要
Abstract
Pneumonia is a common multi-infectious disease that predisposes the elderly and those with weakened immune sys-tems to infection,and early detection can help with later treatment.Factors such as the location,density and clarity of lung le-sions can affect the accuracy of pneumonia image classification.With the development of deep learning,convolutional neural net-work is widely used in medical image classification tasks,however,the learning ability of the network depends on the number of training samples and labels.Aiming at the classification of pneumonia images in computed tomography(CT),a network model based on self-supervised comparative learning(MCLSE)is proposed,which can learn features from unmarked data and improve the accuracy of the network model.Firsly,auxiliary tasks were designed to mine representations from unmarked images to com-plete pre-training,improving the ability of the model to learn data mapping relationships in vector space.Secondly,the convolu-tional neural network is used to extract features.In order to effectively capture higher level feature information,the compression excitation network is selected to improve the classification model and the correlation between the feature channels is modeled.Fi-nally,the trained weights are loaded into the improved classification model,and the network is trained again with marked data in the downstream task.Experiments were carried out on open data sets,SARS-CoV-2 CT and CT Scan for COVID-19 Classifica-tion.The results show that the accuracy of the MCLSE model in this paper for the overall sample classification reached 99.19%and 99.75%,respectively,which was greatly improved compared with the mainstream model.关键词
COVID-19图像/医学图像分类/卷积神经网络/自监督学习/对比学习Key words
COVID-19 image/medical image classification/convolutional neural network/self-supervised learning/contras-tive learning分类
信息技术与安全科学引用本文复制引用
许跃雯,李明,李莉..基于对比学习MocoV2的COVID-19图像分类[J].计算机与现代化,2024,(2):81-87,126,8.基金项目
国家自然科学基金资助项目(61877051,61170192) （61877051,61170192）
重庆市科委重点项目(cstc2017zdcy-zdyf0366) （cstc2017zdcy-zdyf0366）
重庆市教委项目(113143) （113143）
重庆市研究生教改重点项目(yjg182022) （yjg182022）
