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COVID-19 detection from chest X-ray images using Detectron2 and Faster R-CNN

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dc.title COVID-19 detection from chest X-ray images using Detectron2 and Faster R-CNN en
dc.contributor.author Alhaj Ali, Ammar Nassan
dc.contributor.author Katta, Rasin
dc.contributor.author Jašek, Roman
dc.contributor.author Chramcov, Bronislav
dc.contributor.author Krayem, Said
dc.relation.ispartof Lecture Notes in Networks and Systems
dc.identifier.issn 2367-3370 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-031-21438-7
dc.date.issued 2023
utb.relation.volume 597
dc.citation.spage 37
dc.citation.epage 53
dc.event.title 6th Computational Methods in Systems and Software, CoMeSySo 2022
dc.event.location online
dc.event.sdate 2022-10-10
dc.event.edate 2022-10-15
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Science and Business Media Deutschland GmbH
dc.identifier.doi 10.1007/978-3-031-21438-7_3
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-031-21438-7_3
dc.subject COVID-19 en
dc.subject deep learning en
dc.subject Detectron2 en
dc.subject object detection en
dc.subject Faster R-CNN en
dc.description.abstract The COVID-19 outbreak has been causing immense damage to global health and has put the world under tremendous pressure since early 2020. The World Health Organization (WHO) has declared in March 2020 the novel coronavirus outbreak as a global pandemic. Testing of infected patients and early recognition of positive cases is considered a critical step in the fight against COVID-19 to avoid further spreading of this epidemic. As there are no fast and accurate tools available till now for the detection of COVID-19 positive cases, the need for supporting diagnostic tools has increased. Any technological method that can provide rapid and accurate detection will be very useful to medical professionals. However, there are several methods to detect COVID-19 positive cases that are typically performed based on chest X-ray images that contain relevant information about the COVID-19 virus. This paper goal is to introduce a Detectron2 and Faster R-CNN to diagnose COVID-19 automatically from X-ray images. In addition, this study could support non-radiologists with better localization of the disease by visual bounding box. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011441
utb.identifier.obdid 43884993
utb.identifier.scopus 2-s2.0-85148714279
utb.identifier.wok 000992418500003
utb.source d-scopus
dc.date.accessioned 2023-03-20T08:32:19Z
dc.date.available 2023-03-20T08:32:19Z
utb.contributor.internalauthor Alhaj Ali, Ammar Nassan
utb.contributor.internalauthor Katta, Rasin
utb.contributor.internalauthor Jašek, Roman
utb.contributor.internalauthor Chramcov, Bronislav
utb.contributor.internalauthor Krayem, Said
utb.fulltext.sponsorship -
utb.wos.affiliation [Ali, Ammar Alhaj; Katta, Rasin; Jasek, Roman; Chramco, Bronislav; Krayem, Said] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin, Czech Republic
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
utb.fulltext.projects -
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