Using CFW-Net Deep Learning Models for X-Ray Images to Detect COVID-19 Patients

Authors

Wei Wang^1, , Hao Liu¹, Ji Li¹, Hongshan Nie^{2, 3, *,} , Xin Wang^{1, *,}

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

²College of Electrical and Information Engineering, Hunan University, Changsha, 410076, China

³Hunan BJI TECH Co., Ltd., Changsha, 410000, China

^*Corresponding author. Email: hsnie@hnu.edu.cn; wangxin@csust.edu.cn

DOI

10.2991/ijcis.d.201123.001How to use a DOI?

Keywords

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

Abstract

COVID-19 is an infectious disease caused by severe acute respiratory syndrome (SARS)-CoV-2 virus. So far, more than 20 million people have been infected. With the rapid spread of COVID-19 in the world, most countries are facing the shortage of medical resources. As the most extensive detection technology at present, reverse transcription polymerase chain reaction (RT-PCR) is expensive, long-time (time consuming) and low sensitivity. These problems prompted us to propose a deep learning model to help radiologists and clinicians detect COVID-19 cases through chest X-ray. According to the characteristics of chest X-ray image, we designed the channel feature weight extraction (CFWE) module, and proposed a new convolutional neural network, CFW-Net, based on the CFWE module. Meanwhile, in order to improve recognition efficiency, the network adopts three classifiers for classification: one fully connected (FC) layers, global average pooling fully-connected (GFC) module and point convolution global average pooling (CGAP) module. The latter two methods have fewer parameters, less calculation and better real-time performance. In this paper, we have evaluated CFW-Net based on two open-source datasets. The experimental results show that the overall accuracy of our model CFW-Net56-GFC is 94.35% and the accuracy and sensitivity of COVID-19 are 100%. Compared with other methods, our method can detect COVID-19 disease more accurately.

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  - Hao Liu
AU  - Ji Li
AU  - Hongshan Nie
AU  - Xin Wang
PY  - 2020
DA  - 2020/11/27
TI  - Using CFW-Net Deep Learning Models for X-Ray Images to Detect COVID-19 Patients
JO  - International Journal of Computational Intelligence Systems
SP  - 199
EP  - 207
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201123.001
DO  - 10.2991/ijcis.d.201123.001
ID  - Wang2020
ER  -