Sex prediction based on computed tomography images of the mandible using deep learning models

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dc.contributor.authorSecgin, Yusuf
dc.contributor.authorCakmak, Muhammet
dc.contributor.authorSenol, Deniz
dc.contributor.authorOzturk, Oguzhan
dc.contributor.authorKaya, Seren
dc.contributor.authorHarmandaoglu, Oguzhan
dc.contributor.authorOnbas, Omer
dc.date.accessioned2026-07-01T11:39:52Z
dc.date.available2026-07-01T11:39:52Z
dc.date.issued2026
dc.departmentDüzce Üniversitesi
dc.description.abstractThe aim of this study is to estimate sex using deep learning methods from mandibular images obtained from computed tomography (CT) scans. In this study, 2310 images were recorded in jpeg format by segmenting the superior, inferior, anterior, posterior, right side, and left side of the mandible from retrospective and randomly scanned CT images belonging to 184 women and 201 men aged 18-65 years. The obtained data were divided into an 80% training set and a 20% test set, and the performance of the deep learning methods ConvNetBase, InceptionV3, Data-Efficient Transformer (DeiT), and the proposed hybrid model were evaluated and compared. In the study, the hybrid model was found to be the most successful model with a 92.50% accuracy rate, 0.0750 lowest error rate (MAE), 92.48% F1-score value, and 0.95 AUC-ROC value. In terms of accuracy, the hybrid model was followed by InceptionV3 (92.17%), ConvNetBase (88.67%), and DeiT (86%). In the Gradient-weighted Class Activation Mapping (Grad-CAM) analysis, it was determined that the middle and lower mandibular regions contributed significantly to sex prediction. We hope that the hybrid model, which showed a high accuracy rate in our study, will guide forensic doctors and anthropologists in sex prediction.
dc.identifier.doi10.1080/00450618.2026.2617334
dc.identifier.issn0045-0618
dc.identifier.issn1834-562X
dc.identifier.orcid0000-0002-3927-7053
dc.identifier.orcid0000-0002-3752-6642
dc.identifier.orcid0000-0001-6226-9222
dc.identifier.scopus2-s2.0-105031582690
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1080/00450618.2026.2617334
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23518
dc.identifier.wosWOS:001702564200001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofAustralian Journal of Forensic Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260623
dc.subject[Keyword Not Available]
dc.titleSex prediction based on computed tomography images of the mandible using deep learning models
dc.typeArticle

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