International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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“AI-Based Forensic Image Classification of Strangulation Marks

Author(s) Ms. Anisha Darjee, Prof. Prakriti Kaul
Country India
Abstract Strangulation is a critical forensic indicator often encountered in cases of homicidal violence. Accurately identifying strangulation marks is essential for reconstructing the events leading to death, determining the manner of death, and supporting legal proceedings. However, the process of identifying such marks is often subjective, relying heavily on the expertise of forensic pathologists. Variations in interpretation and human error can hinder the accuracy and consistency of forensic conclusions. In recent years, advancements in artificial intelligence (AI), particularly deep learning, have demonstrated promising capabilities in medical and forensic image analysis. This dissertation explores the application of a deep convolutional neural network—ResNet50—to classify strangulation marks from forensic images. The study aims to develop an automated, reliable, and efficient system that assists forensic experts in identifying strangulation patterns with greater objectivity. The methodology involves dataset collection, image preprocessing, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. By leveraging the power of AI in forensic science, this study contributes to the growing body of research focused on integrating technology with forensic pathology, offering a potential tool for improving the speed and precision of forensic assessments in medico-legal contexts.
Keywords Forensic Science, Strangulation Marks, Image Classification, Artificial Intelligence, Deep Learning, ResNet50, Forensic Pathology, Medico-Legal Investigation
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-14
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.48777
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E-ISSN 2582-2160
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