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

  • Datos identificativos

    Identificador: imarina:9138961
    Autores:
    Kamal Sarker MMMakhlouf YBanu SFChambon SRadeva PPuig D
    Resumen:
    © 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/.
  • Otros:

    Autor según el artículo: Kamal Sarker MM; Makhlouf Y; Banu SF; Chambon S; Radeva P; Puig D
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Puig Valls, Domènec Savi
    Resumen: © 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/.
    Áreas temáticas: 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
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: domenec.puig@urv.cat
    Identificador del autor: 0000-0002-0562-4205
    Fecha de alta del registro: 2023-02-19
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://digital-library.theiet.org/content/journals/10.1049/el.2020.1962
    Referencia al articulo segun fuente origial: Electronics Letters. 56 (24): 1298-1301
    Referencia 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
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1049/el.2020.1962
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    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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