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Title: Performance Evaluation of Convolutional Segmentation Models with Human Hand Thermal Images (H2TI) Dataset
Authors: Çevik, M.
Ceylan, M.
Keywords: Infrared imaging
Multi-label segmentation
Thermal segmentation
Thermography (imaging)
Human bodies
Human hands
Multi-label segmentation
Performances evaluation
Segmentation models
Thermal asymmetry
Thermal images
Thermal segmentation
Image segmentation
Issue Date: 2023
Publisher: Springer Science and Business Media Deutschland GmbH
Abstract: Disturbances such as inflammation, edema, and neural activation disorders in the human body lead to local thermal asymmetries. With the recently developed thermal imaging systems, it is possible to detect these asymmetries in the human body. Since the hand performs many functions in human daily activities, it may be subject to abrasion due to overuse. These overuse-related disturbances give a neural and circulatory reaction. Thermal imaging is used to detect these disturbances. In the observation of thermal asymmetries, the regions of interest in the hand should be segmented from the background. When this segmentation process is performed by experts, it may cause problems such as both waste of time and different evaluations. In this study, Human Hand Thermal Image “H2TI” dataset is introduced. Commonly used segmentation models were trained with the H2TI dataset. As a result of the training process, the highest success was achieved by the “Linknet_efficientnet” model with a mIoU (Mean Intersection Over Union) value of 84.5%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Description: 2nd Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2023 -- 2 October 2023 through 2 October 2023 -- -- 302149
ISBN: 9783031456572
ISSN: 0302-9743
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

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