轻量网络MHNet对新冠肺炎的识别研究

Study on the Identification of COVID-19 by Light Weight Network MHNet

为了能够快速准确地诊断出新冠肺炎患者,文章参考MobileNetV2架构并结合注意力网络,改进损失优化函数,依据CNN网络设计准则搭建新型轻量网络MHNet。在COVIDx CXR-2公开数据集上进行的实验表明,该网络在准确率、召回率、特异性、精准率、F1分数、模型大小、CPU单张图推理耗时、GPU单张图推理耗时上的指标分别为92%、99%、85%、86.84%、92.52%、3.91 MB、59.51 ms、17.66 ms。相较于其他传统网络,该网络对新冠肺炎感染者的诊断率较高、诊断效果较好。

In order to diagnose COVID-19 patients more accurately and quickly,this paper refers to MobileNetV2 architecture and combines attention network,improves loss optimization function,and builds a new lightweight network MHNet according to CNN network design criteria.Experiments on a public COVIDx CXR-2 dataset show that the indicators of the network in accuracy,recall,specificity,accuracy,F1 score,model size,CPU single graph reasoning time,GPU single graph reasoning time are 92%,99%,85%,86.84%,92.52%,3.91 MB,59.51 ms,17.66 ms respectively.Compared with other traditional networks,this network has higher diagnostic rate and better diagnostic effect for patients infected with covid-19 infection.