Biological Profile Estimation of the Human Skeleton Based Forensic Identification Using Deep Learning Image Classification

This research study proposes an innovative methodology for the automated estimation of biological profiles through deep learning techniques, with a particular focus on the classification of age and gender derived from chest X-ray imaging. Utilising Convolutional Neural Networks (CNNs) within the TensorFlow/Keras framework, this investigation confronts the inherent limitations associated with traditional forensic anthropology, such as examiner bias, protracted processing durations, and the variability encountered in instances of poorly preserved skeletal remains. The proposed system capitalises on chest X-rays to extend the analytical reach of skeletal assessments beyond the typically examined osseous structures, attaining an impressive 96% accuracy in gender classification alongside negligible error rates in age estimation. These findings highlight the capacity of CNNs to effectively extract and scrutinise intricate anatomical features, thereby facilitating accurate forensic profiling. In contrast to conventional methodologies, this paradigm significantly reduces the necessity for human involvement, presenting a more objective, efficient, and scalable alternative for forensic identification. The incorporation of this system into a user-centric interface enhances its applicability in practical contexts, further illustrating its practical significance. Although this study reveals considerable advancements, subsequent endeavours will concentrate on augmenting the diversity of the dataset and investigating sophisticated architectures to improve generalisability and performance metrics. This research accentuates the transformative potential inherent in AI-driven methodologies within forensic science and medical imaging, establishing a solid foundation for future innovations in these domains.