Secgin, YusufCakmak, MuhammetSenol, DenizOzturk, OguzhanKaya, SerenHarmandaoglu, OguzhanOnbas, Omer2026-07-012026-07-0120260045-06181834-562Xhttps://doi.org/10.1080/00450618.2026.2617334https://hdl.handle.net/20.500.12684/23518The 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.en10.1080/00450618.2026.2617334info:eu-repo/semantics/closedAccess[Keyword Not Available]Article2-s2.0-105031582690WOS:001702564200001Q3Q40000-0002-3927-70530000-0002-3752-66420000-0001-6226-9222