Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Inonu University, Malatya, Türkiye.
- Department of Electrical Electronics Engineering, Gaziantep Islam Science and Technology University, Gaziantep, Türkiye.
- Department of Anatomy, Gaziantep Islam Scıence and Technology Unıversıty, Gaziantep, Türkiye.
- Department of Physical Education and Sport Teaching, Faculty of Sports Sciences, Inonu University, Malatya, 44280, Türkiye.
- Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia. [email protected].
Abstract
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 classification using hand radiographs, offering a rapid and objective alternative to conventional methods. We analyzed 470 left-hand X-ray images from adults aged 18 to 65 years using four convolutional neural network (CNN) architectures: ResNet-18, ResNet-50, InceptionV3, and EfficientNet-B0. Following image preprocessing and data augmentation, models were trained and validated using standard classification metrics: accuracy, precision, recall, and F1 score. Data augmentation included random rotation, horizontal flipping, and brightness adjustments to enhance model generalization. Among the tested models, ResNet-50 achieved the highest classification accuracy (93.2%) with precision of 92.4%, recall of 93.3%, and F1 score of 92.5%. While other models demonstrated acceptable performance, ResNet-50 consistently outperformed them across all metrics. These findings suggest CNNs can reliably extract sexually dimorphic features from hand radiographs. Deep learning approaches, particularly ResNet-50, provide a robust, scalable, and efficient solution for gender prediction from hand X-ray images. This method may serve as a valuable tool in forensic scenarios where speed and reliability are critical. Future research should validate these findings across diverse populations and incorporate explainable AI techniques to enhance interpretability.