Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1068
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dc.contributor.authorÖrnek, Ahmet Haydar-
dc.contributor.authorErvural, Saim-
dc.contributor.authorCeylan, Murat-
dc.contributor.authorKonak, Murat-
dc.contributor.authorSoylu, Hanifi-
dc.contributor.authorSavaşçı, Duygu-
dc.date.accessioned2021-12-13T10:34:38Z-
dc.date.available2021-12-13T10:34:38Z-
dc.date.issued2020-
dc.identifier.issn0765-0019-
dc.identifier.issn1958-5608-
dc.identifier.urihttps://doi.org/10.18280/ts.370409-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1068-
dc.description.abstractMonitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [215E019]en_US
dc.description.sponsorshipThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 215E019).en_US
dc.language.isoenen_US
dc.publisherINT INFORMATION & ENGINEERING TECHNOLOGY ASSOCen_US
dc.relation.ispartofTRAITEMENT DU SIGNALen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectfast correlation-based filteren_US
dc.subjectlocal binary patternen_US
dc.subjectmachine learningen_US
dc.subjectneonateen_US
dc.subjectthermographyen_US
dc.titleClassification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithmsen_US
dc.typeArticleen_US
dc.identifier.doi10.18280/ts.370409-
dc.identifier.scopus2-s2.0-85096507594en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridSoylu, Hanifi/0000-0003-0367-859X-
dc.authorwosidSoylu, Hanifi/AAD-6846-2021-
dc.identifier.volume37en_US
dc.identifier.issue4en_US
dc.identifier.startpage611en_US
dc.identifier.endpage617en_US
dc.identifier.wosWOS:000583751500009en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57210593918-
dc.authorscopusid57195215988-
dc.authorscopusid56276648900-
dc.authorscopusid6506559837-
dc.authorscopusid7003480890-
dc.authorscopusid56444416700-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.openairetypeArticle-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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