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

  • Dades identificatives

    Identificador: imarina:9138961
    Autors:
    Kamal Sarker MMMakhlouf YBanu SFChambon SRadeva PPuig D
    Resum:
    © 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/.
  • Altres:

    Autor segons l'article: Kamal Sarker MM; Makhlouf Y; Banu SF; Chambon S; Radeva P; Puig D
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Banu, Syeda Furruka / Puig Valls, Domènec Savi
    Resum: © 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/.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: syedafurruka.banu@estudiants.urv.cat domenec.puig@urv.cat
    Identificador de l'autor: 0000-0002-5624-1941 0000-0002-0562-4205
    Data d'alta del registre: 2024-11-16
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://digital-library.theiet.org/content/journals/10.1049/el.2020.1962
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Electronics Letters. 56 (24): 1298-1301
    Referència de l'ítem segons les normes 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
    DOI de l'article: 10.1049/el.2020.1962
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2020
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Electrical and Electronic Engineering,Engineering, Electrical & Electronic
    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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