Full metadata record
DC Field | Value | Language |
---|---|---|
dc.creator | Duran-Lopez, L. (Lourdes) | - |
dc.creator | Dominguez-Morales, J.P. (Juan Pedro) | - |
dc.creator | Corral-Jaime, J. (Jesús) | - |
dc.creator | Vicente-Diaz, S. (Saturnino) | - |
dc.creator | Linares-Barranco, A. (Alejandro) | - |
dc.date.accessioned | 2023-03-24T10:50:52Z | - |
dc.date.available | 2023-03-24T10:50:52Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Duran-Lopez, L. (Lourdes); Dominguez-Morales, J.P. (Juan Pedro); Corral-Jaime, J. (Jesús); et al. "COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images". Applied Sciences. 10 (16), 2020,5683 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10171/65786 | - |
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_ES |
dc.description.sponsorship | This work was partially supported by the Spanish grant (with support from the European Regional Development Fund) COFNET (TEC2016-77785-P), and by the Andalusian Regional Project PAIDI2020 (with FEDER support) PROMETEO (AT17_5410_USE). | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | COVID-19 | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Convolutional neural networks | es_ES |
dc.subject | Medical image analysis | es_ES |
dc.subject | Computer-aided diagnosis | es_ES |
dc.subject | X-ray | es_ES |
dc.title | COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.description.note | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.identifier.doi | 10.3390/app10165683 | - |
dadun.citation.number | 16 | es_ES |
dadun.citation.publicationName | Applied Sciences | es_ES |
dadun.citation.startingPage | 5683 | es_ES |
dadun.citation.volume | 10 | es_ES |
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