dc.creator | Durán López, Lourdes | es |
dc.creator | Domínguez Morales, Juan Pedro | es |
dc.creator | Corral Jaime, Jesús | es |
dc.creator | Vicente Díaz, Saturnino | es |
dc.creator | Linares Barranco, Alejandro | es |
dc.date.accessioned | 2020-09-11T10:56:30Z | |
dc.date.available | 2020-09-11T10:56:30Z | |
dc.date.issued | 2020-08 | |
dc.identifier.citation | Durán López, L., Domínguez Morales, J.P., Corral Jaime, J., Vicente Díaz, S. y Linares Barranco, A. (2020). COVID-XNet: a custom Deep Learning system to diagnose and locate COVID-19 in chest X-ray images. Applied Sciences, 10 (16), 5683-. | |
dc.identifier.issn | 2076-3417 | es |
dc.identifier.uri | https://hdl.handle.net/11441/100982 | |
dc.description.abstract | The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19. | es |
dc.description.sponsorship | European Regional Development Fund COFNET TEC2016-77785-P | es |
dc.description.sponsorship | Andalusian Regional (Spain) / FEDER Project PAIDI2020 | es |
dc.description.sponsorship | Andalusian Regional /FEDER PROMETEO AT17-5410-USE | es |
dc.format | application/pdf | es |
dc.format.extent | 12 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Applied Sciences, 10 (16), 5683-. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | COVID-19 | es |
dc.subject | Deep learning | es |
dc.subject | Convolutional neural networks | es |
dc.subject | Medical image analysis | es |
dc.subject | Computer-aided diagnosis | es |
dc.subject | X-ray | es |
dc.title | COVID-XNet: a custom Deep Learning system to diagnose and locate COVID-19 in chest X-ray images | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | COFNET TEC2016-77785-P | es |
dc.relation.projectID | PAIDI2020 | es |
dc.relation.projectID | PROMETEO AT17-5410-USE | es |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/10/16/5683 | es |
dc.identifier.doi | 10.3390/app10165683 | es |
dc.contributor.group | Universidad de Sevilla. TEP108: Robótica y Tecnología de Computadores | es |
idus.validador.nota | This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis | es |
dc.journaltitle | Applied Sciences | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 16 | es |
dc.publication.initialPage | 5683 | es |