A COVID-19 CXR image recognition method based on MSA-DDCovidNet

Wei Wang; Wendi Huang; Xin Wang; Peng Zhang; Nian Zhang

doi:10.1049/ipr2.12474

A COVID-19 CXR image recognition method based on MSA-DDCovidNet

IET Image Process. 2022 Jun 19;16(8):2101-2113. doi: 10.1049/ipr2.12474. Epub 2022 Mar 15.

Authors

Wei Wang¹, Wendi Huang¹, Xin Wang¹, Peng Zhang², Nian Zhang³

Affiliations

¹ School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China.
² School of Electronics and Communications Engineering Sun Yat-sen University Shenzhen China.
³ Department of Electrical and Computer Engineering University of the District of Columbia Washington DC USA.

Abstract

Currently, coronavirus disease 2019 (COVID-19) has not been contained. It is a safe and effective way to detect infected persons in chest X-ray (CXR) images based on deep learning methods. To solve the above problem, the dual-path multi-scale fusion (DMFF) module and dense dilated depth-wise separable (D3S) module are used to extract shallow and deep features, respectively. Based on these two modules and multi-scale spatial attention (MSA) mechanism, a lightweight convolutional neural network model, MSA-DDCovidNet, is designed. Experimental results show that the accuracy of the MSA-DDCovidNet model on COVID-19 CXR images is as high as 97.962%, In addition, the proposed MSA-DDCovidNet has less computation complexity and fewer parameter numbers. Compared with other methods, MSA-DDCovidNet can help diagnose COVID-19 more quickly and accurately.