Sex prediction based on computed tomography images of the mandible using deep learning models
Yazarlar (9)
Yusuf Seçgin Türkiye
Dr. Öğr. Üyesi Muhammet Çakmak Türkiye
Doç. Dr. Deniz Şenol Düzce Üniversitesi, Türkiye
Öğr. Gör. Oğuzhan ÖZTÜRK Kastamonu Üniversitesi, Türkiye
Seren Kaya
Türkiye
Türkiye
Öğr. Gör. Dr. Oğuzhan HARMANDAOĞLU Kastamonu Üniversitesi, Türkiye
Eslem Yaren Bıyık
Dr. Öğr. Üyesi Hatice Yenigül Türkiye
Prof. Dr. Ömer Önbaş Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | AUSTRALIAN JOURNAL OF FORENSIC SCIENCES (Q4) | ||
| Dergi ISSN | 0045-0618 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 02-2026 |
| Cilt / Sayı / Sayfa | – / 0 / 1–23 | DOI | 10.1080/00450618.2026.2617334 |
| Makale Linki | https://doi.org/10.1080/00450618.2026.2617334 | ||
| UAK Araştırma Alanları |
Anatomi
|
||
| Özet |
| The 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 … |
| Anahtar Kelimeler |
| Mandible | ConvNetBase | InceptionV3 | DeiT | hybrid model | sex prediction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Google Scholar | 1 |