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dc.contributor.authorKarakanis, Stefanos
dc.contributor.authorLeontidis, Georgios
dc.date.accessioned2021-01-06T08:24:00Z
dc.date.available2021-01-06T08:24:00Z
dc.date.issued2021-03-01
dc.identifier.citationKarakanis , S & Leontidis , G 2021 , ' Lightweight deep learning models for detecting COVID-19 from chest X-ray images ' , Computers in Biology and Medicine , vol. 130 , 104181 . https://doi.org/10.1016/j.compbiomed.2020.104181en
dc.identifier.issn0010-4825
dc.identifier.otherPURE: 183510328
dc.identifier.otherPURE UUID: 950f5295-6964-4553-a3ef-2a49a7261ba3
dc.identifier.otherScopus: 85098078043
dc.identifier.otherORCID: /0000-0001-6671-5568/work/86450175
dc.identifier.otherPubMed: 33360271
dc.identifier.urihttps://hdl.handle.net/2164/15586
dc.descriptionFunding Information: The authors would like to thank the multiple teams that have contributed to the release of the datasets used in this paper. We would also like to thank the Data Lab, which provided an MSc AI scholarship to the first author, making this project possible.en
dc.format.extent9
dc.language.isoeng
dc.relation.ispartofComputers in Biology and Medicineen
dc.rights© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectGenerative adversarial networksen
dc.subjectDeep neural networken
dc.subjectcovid-19en
dc.subjectmedical informaticsen
dc.subjectCOVID-19en
dc.subjectDeep neural networksen
dc.subjectBacterial pneumoniaen
dc.subjectChest x-raysen
dc.subjectMedical informaticsen
dc.subjectModels, Theoreticalen
dc.subjectLung/diagnostic imagingen
dc.subjectHumansen
dc.subjectMaleen
dc.subjectTomography, X-Ray Computeden
dc.subjectDeep Learningen
dc.subjectSARS-CoV-2en
dc.subjectCOVID-19/diagnostic imagingen
dc.subjectFemaleen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectHealth Informaticsen
dc.subjectComputer Science Applicationsen
dc.subject.lccQA75en
dc.titleLightweight deep learning models for detecting COVID-19 from chest X-ray imagesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.description.statusPeer revieweden
dc.description.versionPreprinten
dc.description.versionPostprinten
dc.identifier.doihttps://doi.org/10.1016/j.compbiomed.2020.104181
dc.date.embargoedUntil2021-12-22
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85098078043&partnerID=8YFLogxKen


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