Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/6037
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ergene, Mehmet Celalettin | - |
dc.contributor.author | Bayrak, Ahmet | - |
dc.contributor.author | Ceylan, Murat | - |
dc.date.accessioned | 2024-08-10T13:37:25Z | - |
dc.date.available | 2024-08-10T13:37:25Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 1768-6733 | - |
dc.identifier.issn | 2116-7176 | - |
dc.identifier.uri | https://doi.org/10.1080/17686733.2024.2364964 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/6037 | - |
dc.description.abstract | Football clubs use various methods such as thermal imaging which is a non-invasive and faster method to detect injuries and increase the success rate of the football club by reducing the injury rate. Studies have proven that with thermal imaging it is possible to detect inflammation caused by an injury. Therefore, it is possible to detect potential injury with infrared thermography. One of the biggest handicaps of injury detection with thermal imaging is that it is open to subjective interpretation, there are many points that can be missed, and it takes time to analyse them one by one. In order to avoid this problem, to increase the success of injury detection, a deep learning supported pipeline has been designed in this study to detect injuries from thermal images. In this pipeline, the hamstring muscle region from the football player thermal images was segmented using U-Net architecture. After that in order to detect injuries, segmented muscle region is classified by using Densenet, Resnet, VGG, Efficientnet architectures variations and feature pyramid added at the end of these architectures. Among the architectures used for classification, the EfficientnetB0 and EfficientnetB1+feature pyramid architectures are the most successful, with accuracies of 83.9% and 81%, respectively. | en_US |
dc.description.sponsorship | This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis Ltd | en_US |
dc.relation.ispartof | Quantitative Infrared Thermography Journal | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Deep learning | en_US |
dc.subject | muscle segmentation | en_US |
dc.subject | injury detection | en_US |
dc.subject | sports medicine | en_US |
dc.subject | thermography | en_US |
dc.title | A new deep learning based end-to-end pipeline for hamstring injury detection in thermal images of professional football player | en_US |
dc.type | Article | en_US |
dc.type | Article; Early Access | en_US |
dc.identifier.doi | 10.1080/17686733.2024.2364964 | - |
dc.identifier.scopus | 2-s2.0-85197506565 | en_US |
dc.department | KTÜN | en_US |
dc.authorid | Ergene, Mehmet Celalettin/0000-0003-1167-146X | - |
dc.authorid | Bayrak, Ahmet/0000-0001-7854-6407 | - |
dc.identifier.wos | WOS:001262788400001 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57193738202 | - |
dc.authorscopusid | 57197708927 | - |
dc.authorscopusid | 56276648900 | - |
item.fulltext | No Fulltext | - |
item.openairetype | Article | - |
item.openairetype | Article; Early Access | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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