Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1068
Title: | Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms | Authors: | Örnek, Ahmet Haydar Ervural, Saim Ceylan, Murat Konak, Murat Soylu, Hanifi Savaşçı, Duygu |
Keywords: | fast correlation-based filter local binary pattern machine learning neonate thermography |
Publisher: | INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC | 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. | URI: | https://doi.org/10.18280/ts.370409 https://hdl.handle.net/20.500.13091/1068 |
ISSN: | 0765-0019 1958-5608 |
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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37.04_09.pdf | 1.37 MB | Adobe PDF | View/Open |
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