Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4760
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Çevik, M. | - |
dc.contributor.author | Ceylan, M. | - |
dc.date.accessioned | 2023-11-11T09:03:39Z | - |
dc.date.available | 2023-11-11T09:03:39Z | - |
dc.date.issued | 2023 | - |
dc.identifier.isbn | 9783031456572 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://doi.org/10.1007/978-3-031-44511-8_6 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/4760 | - |
dc.description | 2nd Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2023 -- 2 October 2023 through 2 October 2023 -- -- 302149 | en_US |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Infrared imaging | en_US |
dc.subject | Multi-label segmentation | en_US |
dc.subject | Thermal segmentation | en_US |
dc.subject | Thermography (imaging) | en_US |
dc.subject | Human bodies | en_US |
dc.subject | Human hands | en_US |
dc.subject | Multi-label segmentation | en_US |
dc.subject | Multi-labels | en_US |
dc.subject | Performances evaluation | en_US |
dc.subject | Segmentation models | en_US |
dc.subject | Thermal | en_US |
dc.subject | Thermal asymmetry | en_US |
dc.subject | Thermal images | en_US |
dc.subject | Thermal segmentation | en_US |
dc.subject | Image segmentation | en_US |
dc.title | Performance Evaluation of Convolutional Segmentation Models with Human Hand Thermal Images (H2TI) Dataset | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1007/978-3-031-44511-8_6 | - |
dc.identifier.scopus | 2-s2.0-85174525255 | en_US |
dc.department | KTÜN | en_US |
dc.identifier.volume | 14298 LNCS | en_US |
dc.identifier.startpage | 80 | en_US |
dc.identifier.endpage | 90 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 58655618500 | - |
dc.authorscopusid | 56276648900 | - |
dc.identifier.scopusquality | Q3 | - |
item.fulltext | No Fulltext | - |
item.openairetype | Conference Object | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
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