A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis

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dc.authorid0000-0001-7549-0137en_US
dc.contributor.authorAslan, Muhammet Fatih
dc.date.accessioned2022-11-08T08:22:03Z
dc.date.available2022-11-08T08:22:03Z
dc.date.issued2022en_US
dc.departmentKMÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000880145900003en_US
dc.descriptionPubMed ID36311473en_US
dc.description.abstractThis paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.en_US
dc.identifier.citationAslan, M. F. (2022). A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis. Chemometrics and Intelligent Laboratory Systems, 231 doi:10.1016/j.chemolab.2022.104695en_US
dc.identifier.doi10.1016/j.chemolab.2022.104695
dc.identifier.issn0169-7439
dc.identifier.pmid36311473
dc.identifier.scopus2-s2.0-85140806756
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.chemolab.2022.104695
dc.identifier.urihttps://hdl.handle.net/11492/6736
dc.identifier.volume231en_US
dc.identifier.wosWOS:000880145900003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAslan, Muhammet Fatih
dc.language.isoen
dc.publisherElsevieren_US
dc.relation.journalChemometrics and Intelligent Laboratory Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlexNeten_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectCOVID-19en_US
dc.subjectDeepLabV3+en_US
dc.subjectSupport Vector Machineen_US
dc.subjectSemantic Segmentationen_US
dc.titleA robust semantic lung segmentation study for CNN-based COVID-19 diagnosisen_US
dc.typeArticle

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