Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1697
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dc.contributor.authorErvural, Saim-
dc.contributor.authorCeylan, Murat-
dc.date.accessioned2022-01-30T17:32:54Z-
dc.date.available2022-01-30T17:32:54Z-
dc.date.issued2022-
dc.identifier.issn1768-6733-
dc.identifier.issn2116-7176-
dc.identifier.urihttps://doi.org/10.1080/17686733.2021.2010379-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1697-
dc.description.abstractMonitoring the body temperatures and evaluating the thermal asymmetry of newborns give an idea about neonatal diseases. Infrared thermography is a non-invasive, non-harmful, and non-contact modality that allows the monitoring of the body temperature distribution. Early diagnosis using a limited data set is extremely vital due to the high mortality rate in newborns and some difficulties in neonatal imaging. Thermography stands out as a useful tool in detecting neonatal diseases compared to other techniques. However, creating a thermogram database consisting of thousands of images from each class required by traditional artificial intelligence methods, is impossible due to the sensitivity of newborns. One of the meta-learning models that has recently gained success in applying limited data learning, especially one-shot, in various fields is Siamese neural networks. In this work, we perform a multi-class classification to provide pre-diagnosis to experts in disease detection using Siamese neural networks. By using two different optimisation techniques and data augmentation, critical diseases with only a few sample data are classified using the method tested in two- and three-class evaluation approaches. The results based on the disease type achieve 99.4% accuracy in infection diseases and 96.4% oesophageal atresia, 97.4% in intestinal atresia, and 94.02% in necrotising enterocolitis.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.publisherTaylor & Francis Ltden_US
dc.relation.ispartofQuantitative Infrared Thermography Journalen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDisease Classificationen_US
dc.subjectNeonatal Intensive Careen_US
dc.subjectOne-Shot Learningen_US
dc.subjectSiamese Neural Networken_US
dc.subjectThermal Imagingen_US
dc.subjectLow-Birth-Weighten_US
dc.subjectInfrared Thermographyen_US
dc.subjectNatural-Historyen_US
dc.subjectSystemen_US
dc.subjectThermoregulationen_US
dc.subjectAnomaliesen_US
dc.subjectInfantsen_US
dc.titleThermogram classification using deep siamese network for neonatal disease detection with limited dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/17686733.2021.2010379-
dc.identifier.scopus2-s2.0-85122016157en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.wosWOS:000736036900001en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57195215988-
dc.authorscopusid56276648900-
dc.identifier.scopusqualityQ3-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
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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