A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

作     者：Xiaorui Zhang Jie Zhou Wei Sun Sunil Kumar Jha 

作者机构：Wuxi Research InstituteNanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital ForensicsMinistry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science&TechnologyNanjing210044China School of AutomationNanjing University of Information Science&TechnologyNanjing 210044 China IT Fundamentals and Education Technologies ApplicationsUniversity of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：1123-1137页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：This work was supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401 in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219 in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund 

主　　题：Convolutional neural networks chest computed tomography image COVID-19 transfer learning mobileNetv2 

摘      要：The key to preventing the COVID-19 is to diagnose patients quickly and *** have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 ***,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 ***,many CNNs have complex structures and massive *** if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread *** solve above problems,this paper proposes a lightweight CNN classification model based on transfer *** the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing *** order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the *** study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by *** results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT *** to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU ***:***/ZhouJie-520/paper-codes.