The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

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dc.contributor.authorAmami, Rimah
dc.contributor.authorSaif, Suleiman Ali Al
dc.contributor.authorAmami, Rim
dc.contributor.authorEleraky, Hassan Ahmed
dc.contributor.authorMelouli, Fatma
dc.contributor.authorBaazaoui, Mariem
dc.coverage.issue1cs
dc.coverage.volume28cs
dc.date.accessioned2022-06-30T07:01:57Z
dc.date.available2022-06-30T07:01:57Z
dc.date.issued2022-06-30cs
dc.description.abstractThe new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architecture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and regularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned previously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70\% which undeniably can contribute effectively to the detection of COVID-19 infection.en
dc.formattextcs
dc.format.extent1-7cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2022 vol. 28, č. 2, s. 1-7. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2022.1.001en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/208124
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/146cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectIncremental trainingen
dc.subjectDCNNen
dc.subjectCovid-19en
dc.subjectResNet-101en
dc.titleThe Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Imagesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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