Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets

Authors

Wei Wang^1, , Xiao Huang^1, , Ji Li¹, Peng Zhang^{2, *,} , Xin Wang^{1, *,}

¹School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

²School of Electronics and Communications Engineering, Sun Yat-sen University, Shenzhen, 518107, China

^*Corresponding authors. Email: zhangpeng5@mail.sysu.edu.cn (P.Z.); wangxin@csust.edu.cn (X. W.)

DOI

10.2991/ijcis.d.210518.001How to use a DOI?

Keywords

COVID-19; Deep learning; MAI-Net; Convolutional neural network; Chest X-ray images

Abstract

COVID-19 is an infectious disease caused by virus SARS-CoV-2 virus. Early classification of COVID-19 is essential for disease cure and control. Transcription-polymerase chain reaction (RT-PCR) is used widely for the detection of COVID-19. However, its high cost, time-consuming and low sensitivity will significantly reduce the diagnosis efficiency and increase the difficulty of diagnosis for COVID-19. For X-ray images of COVID-19 patients have high inter-class similarity and low intra-class variability, we specifically designed a multi attention interaction enhancement module (MAIE) and proposed a new convolutional neural network, MAI-Net, based on this module. As a lightweight network, MAI-Net has fewer layers and amount of network parameters than other network models, enabling more efficient detection of COVID-19. To verify the performance of the model, MAI-Net performed a comparison experiment on two open-source datasets. The experimental results show that its overall accuracy and COVID-19 category accuracy are 96.42% and 100%, respectively, and the sensitivity of COVID-19 is 99.02%. Considering the factors such as accuracy rate, the parameters number of network model and the calculation amount, MAI-Net has better practicability. Compared with the existing work, the network structure of MAI-Net is simpler, and the hardware requirements of the equipment are lower, which can be better used in ordinary equipment.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Wei Wang
AU  - Xiao Huang
AU  - Ji Li
AU  - Peng Zhang
AU  - Xin Wang
PY  - 2021
DA  - 2021/05/28
TI  - Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets
JO  - International Journal of Computational Intelligence Systems
SP  - 1607
EP  - 1616
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210518.001
DO  - 10.2991/ijcis.d.210518.001
ID  - Wang2021
ER  -