COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images

Autores
Orozco, Carlos Ismael; Xamena, Eduardo; Martinez, Cristian Alejandro; Rodriguez, Diego Alejandro
Año de publicación
2021
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Its symptoms are similar to those of the common flu, including fever, cough, dyspnea, myalgia, and fatigue. Due to its rapid expansion globally, the World Health Organization (OMS) declared it a pandemic. The molecular test commonly used worldwide for direct detection of the virus is the RT-PCR test but it takes time to process and the materials used are scarce. In this work we propose: (a) The design and implementation of a deep neural network architecture for the detection of patients with COVID-19 using as input X-ray images of the chest; the architecture is made up of a feature extraction phase, that is, a pre-trained model VGG16 extracts the features of the image; then in the second phase, a multilayer neural network classifies into one of two particular classes (1: COVID, 0: NO COVID). (b) The implementation of a Web platform that allows interested people to use our architecture in a clear, simple and transparent way. The deep learning algorithm was implemented in Python with specific libraries for the design of neural networks, while the Web platform was implemented in PHP using the Laravel framework and MySQL database. We evaluate the performance of our proposal using the sensitivity, specificity and area under the curve (AUC) evaluation metrics, obtaining good results in very short computational times.
Fil: Orozco, Carlos Ismael. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina
Fil: Xamena, Eduardo. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Cristian Alejandro. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina
Fil: Rodriguez, Diego Alejandro. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
DEEP LEARNING
X-RAY TEST
WEB PLATFORM
COVID-19
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/137000
Acceder
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network_name_str CONICET Digital (CONICET)
spelling COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest ImagesOrozco, Carlos IsmaelXamena, EduardoMartinez, Cristian AlejandroRodriguez, Diego AlejandroDEEP LEARNINGX-RAY TESTWEB PLATFORMCOVID-19https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Its symptoms are similar to those of the common flu, including fever, cough, dyspnea, myalgia, and fatigue. Due to its rapid expansion globally, the World Health Organization (OMS) declared it a pandemic. The molecular test commonly used worldwide for direct detection of the virus is the RT-PCR test but it takes time to process and the materials used are scarce. In this work we propose: (a) The design and implementation of a deep neural network architecture for the detection of patients with COVID-19 using as input X-ray images of the chest; the architecture is made up of a feature extraction phase, that is, a pre-trained model VGG16 extracts the features of the image; then in the second phase, a multilayer neural network classifies into one of two particular classes (1: COVID, 0: NO COVID). (b) The implementation of a Web platform that allows interested people to use our architecture in a clear, simple and transparent way. The deep learning algorithm was implemented in Python with specific libraries for the design of neural networks, while the Web platform was implemented in PHP using the Laravel framework and MySQL database. We evaluate the performance of our proposal using the sensitivity, specificity and area under the curve (AUC) evaluation metrics, obtaining good results in very short computational times.Fil: Orozco, Carlos Ismael. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Xamena, Eduardo. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martinez, Cristian Alejandro. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Rodriguez, Diego Alejandro. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaInstitute of Electrical and Electronics Engineers2021-06-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/137000Orozco, Carlos Ismael; Xamena, Eduardo; Martinez, Cristian Alejandro; Rodriguez, Diego Alejandro; COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 19; 6; 8-6-2021; 1-81548-0992CONICET DigitalCONICETspainfo:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/4402info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:20:33Zoai:ri.conicet.gov.ar:11336/137000instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:20:33.364CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
title COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
spellingShingle COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
Orozco, Carlos Ismael
DEEP LEARNING
X-RAY TEST
WEB PLATFORM
COVID-19
title_short COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
title_full COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
title_fullStr COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
title_full_unstemmed COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
title_sort COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images
dc.creator.none.fl_str_mv Orozco, Carlos Ismael
Xamena, Eduardo
Martinez, Cristian Alejandro
Rodriguez, Diego Alejandro
author Orozco, Carlos Ismael
author_facet Orozco, Carlos Ismael
Xamena, Eduardo
Martinez, Cristian Alejandro
Rodriguez, Diego Alejandro
author_role author
author2 Xamena, Eduardo
Martinez, Cristian Alejandro
Rodriguez, Diego Alejandro
author2_role author
author
author
dc.subject.none.fl_str_mv DEEP LEARNING
X-RAY TEST
WEB PLATFORM
COVID-19
topic DEEP LEARNING
X-RAY TEST
WEB PLATFORM
COVID-19
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Its symptoms are similar to those of the common flu, including fever, cough, dyspnea, myalgia, and fatigue. Due to its rapid expansion globally, the World Health Organization (OMS) declared it a pandemic. The molecular test commonly used worldwide for direct detection of the virus is the RT-PCR test but it takes time to process and the materials used are scarce. In this work we propose: (a) The design and implementation of a deep neural network architecture for the detection of patients with COVID-19 using as input X-ray images of the chest; the architecture is made up of a feature extraction phase, that is, a pre-trained model VGG16 extracts the features of the image; then in the second phase, a multilayer neural network classifies into one of two particular classes (1: COVID, 0: NO COVID). (b) The implementation of a Web platform that allows interested people to use our architecture in a clear, simple and transparent way. The deep learning algorithm was implemented in Python with specific libraries for the design of neural networks, while the Web platform was implemented in PHP using the Laravel framework and MySQL database. We evaluate the performance of our proposal using the sensitivity, specificity and area under the curve (AUC) evaluation metrics, obtaining good results in very short computational times.
Fil: Orozco, Carlos Ismael. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina
Fil: Xamena, Eduardo. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Cristian Alejandro. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina
Fil: Rodriguez, Diego Alejandro. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Its symptoms are similar to those of the common flu, including fever, cough, dyspnea, myalgia, and fatigue. Due to its rapid expansion globally, the World Health Organization (OMS) declared it a pandemic. The molecular test commonly used worldwide for direct detection of the virus is the RT-PCR test but it takes time to process and the materials used are scarce. In this work we propose: (a) The design and implementation of a deep neural network architecture for the detection of patients with COVID-19 using as input X-ray images of the chest; the architecture is made up of a feature extraction phase, that is, a pre-trained model VGG16 extracts the features of the image; then in the second phase, a multilayer neural network classifies into one of two particular classes (1: COVID, 0: NO COVID). (b) The implementation of a Web platform that allows interested people to use our architecture in a clear, simple and transparent way. The deep learning algorithm was implemented in Python with specific libraries for the design of neural networks, while the Web platform was implemented in PHP using the Laravel framework and MySQL database. We evaluate the performance of our proposal using the sensitivity, specificity and area under the curve (AUC) evaluation metrics, obtaining good results in very short computational times.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/137000
Orozco, Carlos Ismael; Xamena, Eduardo; Martinez, Cristian Alejandro; Rodriguez, Diego Alejandro; COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 19; 6; 8-6-2021; 1-8
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/137000
identifier_str_mv Orozco, Carlos Ismael; Xamena, Eduardo; Martinez, Cristian Alejandro; Rodriguez, Diego Alejandro; COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 19; 6; 8-6-2021; 1-8
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/4402
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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