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Comprehensive Analysis of the Screening of COVID-19 Approaches in Chest X-ray Images from Portable Devices

dc.contributor.authorIglesias Morís, Daniel
dc.contributor.authorMoura, Joaquim de
dc.contributor.authorNovo Buján, Jorge
dc.contributor.authorOrtega Hortas, Marcos
dc.date.accessioned2024-05-21T07:55:16Z
dc.date.available2024-05-21T07:55:16Z
dc.date.issued2021
dc.identifier.citationD. I. Morís, J. de Moura, J. Novo and M. Ortega, "Comprehensive Analysis of the Screening of COVID-19 Approaches in Chest X-ray Images from Portable Devices", ESANN 2021 proceedings, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Online event, 6-8 October 2021, ISBN 978287587082-7, pp. 165-170. doi: https://doi.org/10.14428/esann/2021.ES2021-31es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36556
dc.descriptionESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningOpen AccessPages 165 - 1702021 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021Virtual, Online 6 October 2021 through 8 October 2021 Code 178821es_ES
dc.description.abstract[Abstract]: Computer-aided diagnosis plays an important role in the COVID-19 pandemic. Currently, it is recommended to use X-ray imaging to diagnose and assess the evolution in patients. Particularly, radiologists are asked to use portable acquisition devices to minimize the risk of cross-infection, facilitating an effective separation of suspected patients with other low-risk cases. In this work, we present an automatic COVID-19 screening, considering 6 representative state-of-the-art deep network architectures on a portable chest X-ray dataset that was specifically designed for this proposal. Exhaustive experimentation demonstrates that the models can separate COVID-19 cases from NON-COVID-19 cases, achieving a 97.68% of global accuracy.es_ES
dc.description.sponsorshipThis research was funded by ISCIII, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; CCEU, Xunta de Galicia through the predoctoral and postdoctoral grant contracts ref. ED481A 2021/196 and ED481B 2021/059, respectively; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from CCEU, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).es_ES
dc.description.sponsorshipXunta de Galicia; ED481A 2021/196es_ES
dc.description.sponsorshipXunta de Galicia; ED481B 2021/059es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2020/24es_ES
dc.description.sponsorshipXunta de Galicia; IN845D 2020/38es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisheri6doc.com publicationes_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 clínicaes_ES
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 OFTALMOLOGICAes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACIÓN Y CARACTERIZACIÓN COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLÓGICA: ESTUDIOS EN ESCLEROSIS MÚLTIPLEes_ES
dc.relation.urihttps://doi.org/10.14428/esann/2021.ES2021-31es_ES
dc.rights© 2021 ESANN Intelligence and Machine Learning. All rights reserved.es_ES
dc.subjectComputer aided diagnosises_ES
dc.subjectNetwork architecturees_ES
dc.titleComprehensive Analysis of the Screening of COVID-19 Approaches in Chest X-ray Images from Portable Deviceses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.startPage165es_ES
UDC.endPage170es_ES
dc.identifier.doi10.14428/esann/2021.ES2021-31
UDC.conferenceTitleESANN 2021es_ES


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