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