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Title: Classification of Medical Thermograms using Transfer Learning
Authors: Örnek, Ahmet Haydar
Ceylan, Murat
Keywords: classification
convolutional neural nets
transfer learning
Issue Date: 2020
Publisher: IEEE
Abstract: Thermal 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.
Description: 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK
ISBN: 978-1-7281-7206-4
ISSN: 2165-0608
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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