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dc.contributor.authorMoura, Joaquim de
dc.contributor.authorRamos, Lucía
dc.contributor.authorVidal, Plácido
dc.contributor.authorNovo Buján, Jorge
dc.contributor.authorOrtega Hortas, Marcos
dc.date.accessioned2020-10-27T18:09:14Z
dc.date.available2020-10-27T18:09:14Z
dc.date.issued2020-08-21
dc.identifier.citationMoura, J.d.; Ramos, L.; Vidal, P.L.; Novo, J.; Ortega, M. Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks. Proceedings 2020, 54, 31. https://doi.org/10.3390/proceedings2020054031es_ES
dc.identifier.issn2504-3900
dc.identifier.urihttp://hdl.handle.net/2183/26558
dc.description.abstract[Abstract] The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.es_ES
dc.description.sponsorshipThis work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the RTI2018-095894-B-I00 research projects, as well as through Ayudas para la formación de profesorado universitario (FPU), Ref. FPU18/02271. Also, this work has received financial support from the European Union (European Regional Development Fund—ERDF) and the Xunta de Galicia, Centro de Investigación del Sistema Universitario de Galicia, Ref. ED431G 2019/01.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherMDPI AGes_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/Plataforma online para prevención y detección precoz de enfermedad vascular mediante análisis automatizado de información e imagen clínica
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICA
dc.relationinfo:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/FPU18%2F02271/ES/
dc.relation.urihttps://doi.org/10.3390/proceedings2020054031es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectComputer-aided diagnosises_ES
dc.subjectChest X-ray imaginges_ES
dc.subjectCOVID-19es_ES
dc.subjectPneumoniaes_ES
dc.subjectDeep learninges_ES
dc.titleAnalysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProceedingses_ES
UDC.volume54es_ES
UDC.issue1es_ES
UDC.startPage31es_ES
dc.identifier.doi10.3390/proceedings2020054031
UDC.conferenceTitle3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020es_ES
UDC.coleccionInvestigación
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.grupoInvGrupo de Visión Artificial e Recoñecemento de Patróns (VARPA)


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