Use of deep learning methods for hand fracture detection from plain hand radiographs

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dc.authoridOnay, Aslıhan/0000-0003-4213-9126
dc.contributor.authorÜreten, Kemal
dc.contributor.authorSevinç, Hüseyin Fatih
dc.contributor.authorİğdeli, Ufuk
dc.contributor.authorOnay, Aslıhan
dc.contributor.authorMaraş, Yüksel
dc.date.accessioned2025-01-21T16:33:16Z
dc.date.available2025-01-21T16:33:16Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractBACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physicians in the diagnosis of hand fractures using deep learning methods. METHODS: In this study, Convolutional Neural Networks (CNN) were used and the transfer learning method was applied. There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset. CNN, a deep learning method, were used in this study. In order to increase the performance of the model, transfer learning was applied with the pre-trained VGG-16, GoogLeNet, and ResNet-50 networks. RESULTS: The accuracy, sensitivity, specificity, and precision results in Group 1 (wrist fracture and normal hand) dataset were 93.3%, 96.8%, 90.3%, and 89.7% , respectively, with VGG-16, were 88.9%, 94.9%, 84.2%, and 82.4%, respectively, with Resnet-50, and were 88.1%, 90.6%, 85.9%, and 85.3%, respectively, with GoogLeNet. The accuracy, sensitivity, specificity, and precision results in Group 2 (phalanx fracture and normal hand) dataset were 84.0%, 84.1%, 83.8%, and 82.8%, respectively, with VGG-16, were 79.4%, 78.5%, 80.3%, and 79.7%, respectively, with Resnet-50, and were 81.7%, 81.3%, 82.1%, and 81.3%, respectively, with GoogLeNet. CONCLUSION: We achieved promising results in this CAD method, which we developed by applying methods such as transfer learning, data augmentation, which are state-of-the-art practices in deep learning applications. This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.
dc.identifier.doi10.14744/tjtes.2020.06944
dc.identifier.endpage201
dc.identifier.issn1306-696X
dc.identifier.issue2
dc.identifier.pmid35099027
dc.identifier.scopus2-s2.0-85123962757
dc.identifier.scopusqualityQ3
dc.identifier.startpage196
dc.identifier.trdizinid1136720
dc.identifier.urihttps://doi.org/10.14744/tjtes.2020.06944
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay1136720
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23771
dc.identifier.volume28
dc.identifier.wosWOS:000750361500011
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherTurkish Assoc Trauma Emergency Surgery
dc.relation.ispartofUlusal Travma Ve Acil Cerrahi Dergisi-Turkish Journal of Trauma & Emergency Surgery
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectComputer-aided diagnosis; convolutional neural networks; data augmentation; deep learning; hand fractures; transfer learning
dc.titleUse of deep learning methods for hand fracture detection from plain hand radiographs
dc.title.alternativeDüz el radyografilerinden el kırıklarının tespiti için derin öğrenme yöntemlerinin kullanılması
dc.typeArticle

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