Weapon identification based on wound characteristics plays a vital role in forensic investigations, helping experts
reconstruct crime events and determine the nature of injuries. Traditional wound examination relies heavily on forensic
expertise and manual interpretation, which may be time-consuming and subject to observer variability. This study
proposes a deep ensemble learning framework for automated weapon recognition using forensic wound pattern images.
The proposed system integrates multiple convolutional neural network models, namely ResNet50, EfficientNetB0, and
DenseNet121, to extract discriminative wound features and improve classification performance through an ensemble
voting strategy. Wound images corresponding to different weapon categories, including gunshot, knife, blunt-force,
and puncture injuries, are preprocessed using image enhancement and segmentation techniques to improve feature
visibility. The ensemble model is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results
demonstrate that the proposed framework achieves superior recognition performance compared to individual deep
learning models, providing reliable identification of injury-causing weapons. The findings highlight the potential of
artificial intelligence-assisted forensic analysis as a decision-support tool for investigators and forensic practitioners.
The proposed approach contributes toward improving the efficiency, consistency, and objectivity of forensic wound
assessment in modern criminal investigations.
Publication Date: 2026-06-13