Densely attention mechanism based network for COVID-19 detection in chest X-rays

Article

Ullah, Zahid, Usman, Muhammad, Latif, Siddique and Gwak, Jeonghwan. 2023. "Densely attention mechanism based network for COVID-19 detection in chest X-rays." Scientific Reports. 13 (1). https://doi.org/10.1038/s41598-022-27266-9
Article Title

Densely attention mechanism based network for COVID-19 detection in chest X-rays

ERA Journal ID201487
Article CategoryArticle
AuthorsUllah, Zahid, Usman, Muhammad, Latif, Siddique and Gwak, Jeonghwan
Journal TitleScientific Reports
Journal Citation13 (1)
Article Number261
Number of Pages14
Year2023
PublisherNature Publishing Group
Place of PublicationUnited Kingdom
ISSN2045-2322
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-022-27266-9
Web Address (URL)https://www.nature.com/articles/s41598-022-27266-9
AbstractAutomatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.
KeywordsCOVID‑19; chest X‑rays
ANZSRC Field of Research 20204299. Other health sciences
Byline AffiliationsKorea National University of Transportation, South Korea
Seoul National University, Korea
University of Southern Queensland
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