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Sex Identification Using Computed Tomography Images of the Human Skull.

Ricardo Nicida Kazama, Gabriel Shimada Belem, Eduardo L L Cabral

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary

    Machine learning accurately identifies sex from skull images using convolutional neural networks (CNNs), improving forensic and archaeological identification. This AI approach offers a faster, more scalable alternative to traditional methods.

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    Area of Science:

    • Forensic Anthropology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Facial reconstruction from skulls is crucial for identification in forensics, archaeology, and medicine.
    • Traditional biological profiling methods are time-consuming and require specialized expertise.

    Purpose of the Study:

    • To develop a machine learning model for accurate sex identification from skull images.
    • To evaluate the performance of different convolutional neural network (CNN) architectures for this task.

    Main Methods:

    • Utilized a transfer learning approach with pre-trained CNNs (VGG, Inception, ResNet) on ImageNet.
    • Trained a classification layer on a dataset of computed tomography (CT) skull images.
    • Evaluated model performance using accuracy metrics on a test dataset.

    Main Results:

    • The ResNet152 model achieved the highest accuracy at 93.46% on the test dataset.
    • Performance matched state-of-the-art results in skull-based sex identification.
    • Demonstrated the effectiveness of CNNs for biological profiling.

    Conclusions:

    • Machine learning, particularly CNNs, significantly enhances the efficiency and accuracy of sex identification from skull images.
    • This technology offers a scalable and precise tool for forensic, archaeological, and medical applications.
    • The approach reduces reliance on manual methods, benefiting time-sensitive and resource-limited scenarios.