Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/581
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dc.contributor.authorErvural, Saim-
dc.contributor.authorCeylan, Murat-
dc.date.accessioned2021-12-13T10:26:58Z-
dc.date.available2021-12-13T10:26:58Z-
dc.date.issued2022-
dc.identifier.issn1380-7501-
dc.identifier.issn1573-7721-
dc.identifier.urihttps://doi.org/10.1007/s11042-021-11391-0-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/581-
dc.description.abstractEvaluation of body temperature and thermal symmetry in neonates is important in monitoring health conditions and predicting potential risks. With thermography, which is a harmless and noncontact method, diseases in neonates can be detected at an early stage using appropriate artificial intelligence techniques. Medical imaging is limited due to neonates' sensitivity to the thermal environment. This study proposes a classification model for classification problems with limited data (specifically, neonatal diseases) using data augmentation and artificial intelligence methodology. In the study, a multi-class classification was performed by combining images produced by data augmentation and employing the ability of convolutional neural networks to learn important features from the images, with 4 classes ranging from 8 to 16 newborns in each class. That is, there are four classes: 34 neonatal with abdominal, cardiovascular, and pulmonary abnormalities and 10 neonatal undiagnosed (premature). The dataset was created by taking 20 images from each of the 44 neonates. To test the performance of the proposed method, six different data separation experiments were conducted. Although the best classification accuracy is 94%, the 89% value obtained in the experiment when the model was tested with image samples of babies that had not been used in training the model is more significant for the model.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.publisherSPRINGERen_US
dc.relation.ispartofMULTIMEDIA TOOLS AND APPLICATIONSen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Network (Cnn)en_US
dc.subjectInfrared Thermography (Irt)en_US
dc.subjectNeonatal Diseasesen_US
dc.subjectClassificationen_US
dc.subjectData Augmentationen_US
dc.subjectLow-Birth-Weighten_US
dc.subjectInfrared Thermographyen_US
dc.subjectThermoregulationen_US
dc.subjectTemperatureen_US
dc.subjectInfantsen_US
dc.subjectExperienceen_US
dc.subjectNewbornsen_US
dc.subjectSystemen_US
dc.subjectNecen_US
dc.subjectCten_US
dc.titleClassification of neonatal diseases with limited thermal Image dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s11042-021-11391-0-
dc.identifier.scopus2-s2.0-85112599964en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridervural, saim/0000-0003-4104-1928-
dc.identifier.wosWOS:000682496300001en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57195215988-
dc.authorscopusid56276648900-
dc.identifier.scopusqualityQ1-
item.fulltextWith Fulltext-
item.grantfulltextembargo_20300101-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.cerifentitytypePublications-
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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