Please use this identifier to cite or link to this item:
Title: Deep Learning Based Super Resolution and Classification Applications for Neonatal Thermal Images
Authors: Şenalp, Fatih Mehmet
Ceylan, Murat
Keywords: Classification
Deep Learning
Thermal Imaging
Issue Date: 2021
Publisher: Int Information & Engineering Technology Assoc
Abstract: The thermal camera systems can be used in all kinds of applications that require the detection of heat change, but thermal imaging systems are highly costly systems. In recent years, developments in the field of deep learning have increased the success by obtaining quality results compared to traditional methods. In this paper, thermal images of neonates (healthy unhealthy) obtained from a high-resolution thermal camera were used and these images were evaluated as high resolution (ground truth) images. Later, these thermal images were downscaled at 1/2, 1/4, 1/8 ratios, and three different datasets consisting of low-resolution images in different sizes were obtained. In this way, super-resolution applications have been carried out on the deep network model developed based on generative adversarial networks (GAN) by using three different datasets. The successful performance of the results was evaluated with PSNR (peak signal to noise ratio) and SSIM (structural similarity index measure). In addition, healthy unhealthy classification application was carried out by means of a classifier network developed based on convolutional neural networks (CNN) to evaluate the super-resolution images obtained using different datasets. The obtained results show the importance of combining medical thermal imaging with super-resolution methods.
ISSN: 0765-0019
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

Files in This Item:
File SizeFormat 
38.05_11.pdf1.36 MBAdobe PDFView/Open
Show full item record

CORE Recommender


checked on Jan 30, 2023

Page view(s)

checked on Feb 6, 2023


checked on Feb 6, 2023

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.