Author, as appears in the article.: Kamal Sarker MM; Makhlouf Y; Banu SF; Chambon S; Radeva P; Puig D
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Banu, Syeda Furruka / Puig Valls, Domènec Savi
Keywords: Reduced inequalities
Abstract: © The Institution of Engineering and Technology 2020 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/.
Thematic Areas: Química Odontología Materiais Matemática / probabilidade e estatística Interdisciplinar Engineering, electrical & electronic Engenharias iv Engenharias iii Engenharias ii Enfermagem Electrical and electronic engineering Educação Ciências biológicas iii Ciências ambientais Ciências agrárias i Ciência da computação Astronomia / física
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat
Author identifier: 0000-0002-5624-1941 0000-0002-0562-4205
Record's date: 2024-11-23
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://digital-library.theiet.org/content/journals/10.1049/el.2020.1962
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Electronics Letters. 56 (24): 1298-1301
APA: Kamal Sarker MM; Makhlouf Y; Banu SF; Chambon S; Radeva P; Puig D (2020). Web-based efficient dual attention networks to detect COVID-19 from X-ray images. Electronics Letters, 56(24), 1298-1301. DOI: 10.1049/el.2020.1962
Article's DOI: 10.1049/el.2020.1962
Entity: Universitat Rovira i Virgili
Journal publication year: 2020
Publication Type: Journal Publications
Repositori URV