Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19
Impacte
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadades
Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
comunitat-uji-handle4:
INVESTIGACIONMetadades
Títol
Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19Autoria
Data de publicació
2022-08Editor
ElsevierISSN
1568-4946Cita bibliogràfica
Mar-Cupido R, García V, Rivera G, Sánchez JS. Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19. Appl Soft Comput. 125 (2022). doi: 10.1016/j.asoc.2022.109207.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://www.sciencedirect.com/science/article/pii/S1568494622004410Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for ... [+]
The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss. [-]
Publicat a
Applied Soft Computing, 2022, vol. 125Entitat finançadora
Universitat Jaume I
Codi del projecte o subvenció
UJI-B2018-49
Drets d'accés
Copyright © 2022 Elsevier B.V. All rights reserved.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
Apareix a les col.leccions
- INIT_Articles [752]