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SC2Net: a novel segmentation-based classification network for detection of COVID-19 in chest X-ray images.

Zhao, Huimin; Fang, Zhenyu; Ren, Jinchang; Maclellan, Calum; Xia, Yong; Li, Shuo; Sun, Meijun; Ren, Kevin

Authors

Huimin Zhao

Zhenyu Fang

Jinchang Ren

Calum Maclellan

Yong Xia

Shuo Li

Meijun Sun

Kevin Ren



Abstract

The pandemic of COVID-19 has become a global crisis in public health, which has led to a massive number of deaths and severe economic degradation. To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial. As the popularly used real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test can be lengthy and inaccurate, chest screening with radiography imaging is still preferred. However, due to limited image data and the difficulty of the early-stage diagnosis, existing models suffer from ineffective feature extraction and poor network convergence and optimisation. To tackle these issues, a segmentation-based COVID-19 classification network, namely SC2Net, is proposed for effective detection of the COVID-19 from chest x-ray (CXR) images. The SC2Net consists of two subnets: a COVID-19 lung segmentation network (CLSeg), and a spatial attention network (SANet). In order to supress the interference from the background, the CLSeg is first applied to segment the lung region from the CXR. The segmented lung region is then fed to the SANet for classification and diagnosis of the COVID-19. As a shallow yet effective classifier, SANet takes the ResNet-18 as the feature extractor and enhances highlevel feature via the proposed spatial attention module. For performance evaluation, the COVIDGR 1.0 dataset is used, which is a high-quality dataset with various severity levels of the COVID-19. Experimental results have shown that, our SC2Net has an average accuracy of 84.23% and an average F1 score of 81.31% in detection of COVID-19, outperforming several state-of-the-art approaches.

Citation

ZHAO, H., FANG, Z., REN, J., MACLELLAN, C., XIA, Y., SUN, M. and REN, K. 2022. SC2Net: a novel segmentation-based classification network for detection of COVID-19 in chest X-ray images. IEEE journal of biomedical and health informatics [online], 26(8), pages 4032-4043. Available from: https://doi.org/10.1109/JBHI.2022.3177854

Journal Article Type Article
Acceptance Date May 25, 2022
Online Publication Date May 25, 2022
Publication Date Aug 31, 2022
Deposit Date May 26, 2022
Publicly Available Date May 26, 2022
Journal IEEE Journal of Biomedical and Health Informatics
Print ISSN 2168-2194
Electronic ISSN 2168-2208
Publisher IEEE Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 26
Issue 8
Pages 4032-4043
DOI https://doi.org/10.1109/JBHI.2022.3177854
Keywords COVID-19; Chest X-ray imaging; SC2Ne; Lung segmentation; ResNet-18; Pulmonary diseases; Lung; Feature extraction; Image segmentation; X-ray imaging; Training
Public URL https://rgu-repository.worktribe.com/output/1674621

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