Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1071
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dc.contributor.authorÖrnek, Ahmet Haydar-
dc.contributor.authorCeylan, Murat-
dc.date.accessioned2021-12-13T10:34:39Z-
dc.date.available2021-12-13T10:34:39Z-
dc.date.issued2020-
dc.identifier.isbn978-1-7281-7206-4-
dc.identifier.issn2165-0608-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/1071-
dc.description28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORKen_US
dc.description.abstractThermal imaging has been used for decades to monitor the health status of neonates as an non-invasive and non-ionizing imaging technique. Applications such as thermal asymmetry and disease analysis can be performed by applying deep learning methods to thermal imaging technique. However, thousands of different images are needed to perform analyzes with deep learning methods. It takes many years to create data sets with thousands of different images due to feeding time, medication time and instant baby care in the neonatal intensive care unit. In this study, a unhealthy-healthy classification was performed using thermal images obtained from the Selcuk University, Faculty of Medicine, Neonatal Intensive Care Unit for one year. Transfer learning method has been used to overcome the lack of data problem. When VGG16 model was used for transfer learning, the results were obtained as 100% sensitivity and 94.73% specificity. This result shows that thermal imaging and transfer learning method can be used in early diagnosis of diseases.en_US
dc.description.sponsorshipIstanbul Medipol Univen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectclassificationen_US
dc.subjectconvolutional neural netsen_US
dc.subjectthermographyen_US
dc.subjecttransfer learningen_US
dc.subjectneonateen_US
dc.titleClassification of Medical Thermograms using Transfer Learningen_US
dc.typeConference Objecten_US
dc.identifier.scopus2-s2.0-85100305889en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.wosWOS:000653136100006en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1tr-
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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