Ensemble-LungMaskNet: Automated lung segmentation using ensembled deep encoders

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dc.authorscopusid56780068800
dc.authorscopusid35617556600
dc.authorscopusid55666247200
dc.contributor.authorÖksüz, Coşku
dc.contributor.authorUrhan, Oğuzhan
dc.contributor.authorGüllü, Mehmet Kemal
dc.date.accessioned2022-02-15T16:58:12Z
dc.date.available2022-02-15T16:58:12Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.descriptionKocaeli University;Kocaeli University Technoparken_US
dc.description2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- -- 172175en_US
dc.description.abstractAutomated lung segmentation has importance because it gives clues about several diseases to the experts. It is the step that comes before further detailed analyses of the lungs. However, segmentation of the lungs is a challenging task since the opacities and consolidations are caused by various lung diseases. As a result, the clarity of the borders of the lungs may be lost which makes the segmentation task difficult. The presence of various medical equipment such as cables in the image is another factor that makes segmentation difficult. Therefore, it is a necessity to develop methods that can handle such situations. Learning the most useful patterns related to various diseases is possible with deep learning methods. Unlike conventional methods, learning the patterns improves the generalization ability of the models on unseen data. For this purpose, a deep segmentation framework including ensembles of pre-trained lightweight networks is proposed for lung region segmentation in this work. The experimental results achieved on two publicly available data sets demonstrate the effectiveness of the proposed framework. © 2021 IEEE.en_US
dc.identifier.doi10.1109/INISTA52262.2021.9548367
dc.identifier.isbn9781665436038
dc.identifier.scopus2-s2.0-85116691459en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/INISTA52262.2021.9548367
dc.identifier.urihttps://hdl.handle.net/20.500.14034/365
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.journal2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCOVID-19en_US
dc.subjectDeep learningen_US
dc.subjectEnsembleen_US
dc.subjectLung segmentationen_US
dc.subjectX-Rayen_US
dc.subjectDeep learningen_US
dc.subjectImage segmentationen_US
dc.subjectMedical imagingen_US
dc.subjectSignal encodingen_US
dc.subjectConventional methodsen_US
dc.subjectCOVID-19en_US
dc.subjectDeep learningen_US
dc.subjectEnsembleen_US
dc.subjectGeneralization abilityen_US
dc.subjectLearning methodsen_US
dc.subjectLung regionsen_US
dc.subjectLung segmentationen_US
dc.subjectRegion segmentationen_US
dc.subjectUseful patternsen_US
dc.subjectBiological organsen_US
dc.titleEnsemble-LungMaskNet: Automated lung segmentation using ensembled deep encodersen_US
dc.typeConference Objecten_US

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