Deep Learning for Sex Estimation from Whole-Foot X-Rays: Benchmarking CNNs for Rapid Forensic Identification

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dc.contributor.authorCiftci, Rukiye
dc.contributor.authorAtik, Ipek
dc.contributor.authorEken, Ozgur
dc.contributor.authorAldhahi, Monira I.
dc.date.accessioned2026-04-04T13:31:09Z
dc.date.available2026-04-04T13:31:09Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground: Accurate sex estimation is crucial in forensic identification when DNA analysis is impractical or remains are fragmented. Traditional anthropometric approaches often rely on single bone measurements and yield moderate levels of accuracy. Objective: This study aimed to evaluate deep convolutional neural networks (CNNs) for automated sex estimation using entire foot radiographs, an approach rarely explored. Methods: Digital foot radiographs from 471 adults (238 men, 233 women, aged 18-65 years) without deformities or prior surgery were retrospectively collected at a single tertiary center. Six CNN architectures (AlexNet, ResNet-18, ResNet-50, ShuffleNet, GoogleNet, and InceptionV3) were trained using transfer learning (70/15/15 train-validation-test split, data augmentation). The model performance was assessed using accuracy, sensitivity, specificity, precision, and F1-score. Results: InceptionV3 achieved the highest accuracy (97.1%), surpassing previously reported methods (typically 72-89%), with balanced sensitivity (97.5%) and specificity (96.8%). ResNet-50 followed closely (95.7%), whereas simpler networks, such as AlexNet, underperformed (90%). Conclusions: Deep learning applied to whole-foot radiographs delivers state-of-the-art accuracy for sex estimation, enabling rapid, reproducible, and cost-effective forensic identification when DNA analysis is delayed or unavailable, such as in mass disasters or clinical emergency settings.
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia [PNURSP2025R286]
dc.description.sponsorshipWe extend our gratitude to the participants for their invaluable contribution to this study. Special Thanks to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R286), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
dc.identifier.doi10.3390/diagnostics15222923
dc.identifier.issn2075-4418
dc.identifier.issue22
dc.identifier.orcid0000-0002-5488-3158
dc.identifier.orcid0000-0002-9761-1347
dc.identifier.orcid0000-0002-5255-4860
dc.identifier.pmid41300947
dc.identifier.scopus2-s2.0-105023128850
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics15222923
dc.identifier.urihttps://hdl.handle.net/11616/108608
dc.identifier.volume15
dc.identifier.wosWOS:001624615800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectsex estimation
dc.subjectfoot radiographs
dc.subjectforensic applications
dc.subjectconvolutional neural networks (CNNs)
dc.subjectfoot
dc.subjectdeep learning benchmarking
dc.titleDeep Learning for Sex Estimation from Whole-Foot X-Rays: Benchmarking CNNs for Rapid Forensic Identification
dc.typeArticle

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