Sex estimation based on frontal sinus computed tomography images using machine learning and artificial neural networks
Yazarlar (6)
Seren Kaya
Türkiye
Türkiye
Öğr. Gör. Oğuzhan HARMANDAOĞLU Kastamonu Üniversitesi, Türkiye
Öğr. Gör. Oğuzhan ÖZTÜRK Kastamonu Üniversitesi, Türkiye
Yusuf Seçgin Karabük Üniversitesi, Türkiye
Doç. Dr. Deniz Şenol Düzce Üniversitesi, Türkiye
Ömer Önbaş Düzce Üniversitesi, 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 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 09-2025 |
| Cilt / Sayı / Sayfa | 58 / 1 / 1–14 | DOI | 10.1080/00450618.2025.2561625 |
| Makale Linki | https://doi.org/10.1080/00450618.2025.2561625 | ||
| Özet |
| Due to its anatomical uniqueness, the frontal sinus (FS) shows significant inter-individual differences by ancestry, age, and sex, making it useful for preliminary identification processes. This study aims to estimate sex using machine learning (ML) algorithms and artificial neural networks (ANN) applied to morphometric data from FS computed tomography (CT) images. This retrospective study analysed CT scans of 338 females and 338 males aged 18–65. FS measurements comprised sinus floor anteroposterior length, volume, area, height, depth, width, and anterior wall thickness (AWT). Sex estimation was performed using several ML algorithms, including Linear Discriminant Analysis, Quadratic Discriminant Analysis, Logistic Regression, Extra Trees Classifier, Decision Tree, Random Forest, k-Nearest Neighbours, and Gaussian Naive Bayes. Additionally, a multilayer perceptron classifier, representing ANN models … |
| Anahtar Kelimeler |
| Frontal sinus | artificial neural networks | machine learning | sex estimation | paranasal sinuses |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Google Scholar | 3 |