Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/2419
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Örnek, Ahmet Haydar | - |
dc.contributor.author | Ceylan, Murat | - |
dc.date.accessioned | 2022-05-23T20:22:42Z | - |
dc.date.available | 2022-05-23T20:22:42Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1380-7501 | - |
dc.identifier.issn | 1573-7721 | - |
dc.identifier.uri | https://doi.org/10.1007/s11042-021-11852-6 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/2419 | - |
dc.description.abstract | Infrared thermal imaging and deep learning provide intelligent monitoring systems that detect diseases in early phases. However, deep learning models require thousands of labeled images to be effectively trained from scratch. Since such a dataset cannot be collected from a neonatal intensive care unit (NICU), deep transfer learning models and methods were used for the first time in this study to classify neonates in the NICU as healthy and unhealthy. When nine different pre-trained models (VGG16, VGG19, Xception, ResNet101, ResNet50, Inceptionv3, InceptionResNetv2, MobileNet and DenseNet201) and two different classification methods (Multilayer Perceptrons and Support Vector Machines (SVMs)) were compared, best results were obtained as 100.00% specificity, sensitivity and accuracy with VGG19, and SVMs. This study proposes highest classification performance when comparing other studies that detect health status of neonates. | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (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 | Springer | en_US |
dc.relation.ispartof | Multimedia Tools And Applications | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Classification | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Medicine | en_US |
dc.subject | Neonate | en_US |
dc.subject | Thermography | en_US |
dc.subject | Transfer learning | en_US |
dc.subject | Svm | en_US |
dc.title | Medical thermograms' classification using deep transfer learning models and methods | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/s11042-021-11852-6 | - |
dc.identifier.scopus | 2-s2.0-85124306887 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.volume | 81 | en_US |
dc.identifier.issue | 7 | en_US |
dc.identifier.startpage | 9367 | en_US |
dc.identifier.endpage | 9384 | en_US |
dc.identifier.wos | WOS:000751584600004 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
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 |
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File | Size | Format | |
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s11042-021-11852-6.pdf Until 2030-01-01 | 2.52 MB | Adobe PDF | View/Open Request a copy |
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