Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models

Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models

Abstract Background Accurate gender estimation plays a crucial role in forensic identification, especially in mass disasters or cases involving fragmented or decomposed remains where traditional skeletal landmarks are unavailable. This study aimed to develop a deep learning-based model for gender cl...

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Bibliographic Details
Main Authors: Hilal Er Ulubaba, İpek Atik, Rukiye Çiftçi, Özgür Eken, Monira I. Aldhahi
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Medical Imaging
Subjects:
Gender prediction
Hand radiograph
Deep learning
Convolutional neural network (CNN)
Forensic identification
ResNet-50
Online Access:https://doi.org/10.1186/s12880-025-01809-8
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https://doi.org/10.1186/s12880-025-01809-8

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