Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1068
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Örnek, Ahmet Haydar | - |
dc.contributor.author | Ervural, Saim | - |
dc.contributor.author | Ceylan, Murat | - |
dc.contributor.author | Konak, Murat | - |
dc.contributor.author | Soylu, Hanifi | - |
dc.contributor.author | Savaşçı, Duygu | - |
dc.date.accessioned | 2021-12-13T10:34:38Z | - |
dc.date.available | 2021-12-13T10:34:38Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 0765-0019 | - |
dc.identifier.issn | 1958-5608 | - |
dc.identifier.uri | https://doi.org/10.18280/ts.370409 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1068 | - |
dc.description.abstract | Monitoring 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.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [215E019] | en_US |
dc.description.sponsorship | This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 215E019). | en_US |
dc.language.iso | en | en_US |
dc.publisher | INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC | en_US |
dc.relation.ispartof | TRAITEMENT DU SIGNAL | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | fast correlation-based filter | en_US |
dc.subject | local binary pattern | en_US |
dc.subject | machine learning | en_US |
dc.subject | neonate | en_US |
dc.subject | thermography | en_US |
dc.title | Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.18280/ts.370409 | - |
dc.identifier.scopus | 2-s2.0-85096507594 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Soylu, Hanifi/0000-0003-0367-859X | - |
dc.authorwosid | Soylu, Hanifi/AAD-6846-2021 | - |
dc.identifier.volume | 37 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.startpage | 611 | en_US |
dc.identifier.endpage | 617 | en_US |
dc.identifier.wos | WOS:000583751500009 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57210593918 | - |
dc.authorscopusid | 57195215988 | - |
dc.authorscopusid | 56276648900 | - |
dc.authorscopusid | 6506559837 | - |
dc.authorscopusid | 7003480890 | - |
dc.authorscopusid | 56444416700 | - |
dc.identifier.scopusquality | Q3 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.04. 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 |
Files in This Item:
File | Size | Format | |
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37.04_09.pdf | 1.37 MB | Adobe PDF | View/Open |
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