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

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 (IET)
• 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