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Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images.

Sarker, Md. Mostafa Kamal; Makhlouf, Yasmine; Banu, Syeda Furruka; Chambon, Sylvie; Radeva, Petia; Puig, Domenec

Authors

Yasmine Makhlouf

Syeda Furruka Banu

Sylvie Chambon

Petia Radeva

Domenec Puig



Abstract

Rapid and accurate detection of COVID-19 is a crucial step to control the virus. For this purpose, the authors designed a web-based COVID-19 detector using efficient dual attention networks, called ‘EDANet’. The EDANet architecture is based on inverted residual structures to reduce the model complexity and dual attention mechanism with position and channel attention blocks to enhance the discriminant features from the different layers of the network. Although the EDANet has only 4.1 million parameters, the experimental results demonstrate that it achieves the state-of-the-art results on the COVIDx data set in terms of accuracy and sensitivity of 96 and 94%. The web application is available at the following link: https://covid19detector-cxr.herokuapp.com/.

Citation

SARKER, M.M.K., MAKHLOUF, Y., BANU, S.F., CHAMBON, S., RADEVA, P. and PUIG, D. 2020. Web-based efficient dual attention networks to detect COVID-19 from X-ray images. Electronics letters [online], 56(24), pages 1298-1301. Available from: https://doi.org/10.1049/el.2020.1962

Journal Article Type Article
Acceptance Date Oct 16, 2020
Online Publication Date Oct 21, 2020
Publication Date Nov 30, 2020
Deposit Date Dec 2, 2021
Publicly Available Date Dec 9, 2021
Journal Electronics Letters
Print ISSN 0013-5194
Electronic ISSN 1350-911X
Publisher Institution of Engineering and Technology
Peer Reviewed Peer Reviewed
Volume 56
Issue 24
Pages 1298-1301
DOI https://doi.org/10.1049/el.2020.1962
Keywords Internet; Object detection; X-ray imaging; Medical image processing; Web-based efficient dual attention networks; X-ray images; Web-based COVID-19 detector; EDANet architecture; Inverted residual structures; Dual attention mechanism; COVIDx data set; Mode
Public URL https://rgu-repository.worktribe.com/output/1538638

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Licence
https://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
This paper is a postprint of a paper submitted to and accepted for publication in [journal] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.







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