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

  • Identification data

    Identifier: imarina:9138961
    Handle: https://hdl.handle.net/20.500.11797/imarina9138961
    Authors:
    Kamal Sarker MMMakhlouf YBanu SFChambon SRadeva PPuig D
    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/.

  • Others:

    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
    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
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications

  • Keywords:

    Electrical and Electronic Engineering,Engineering, Electrical & Electronic
    Reduced inequalities
    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

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