University of Oulu

Oura, P., Junno, A. & Junno, JA. Deep learning in forensic gunshot wound interpretation—a proof-of-concept study . Int J Legal Med 135, 2101–2106 (2021). https://doi.org/10.1007/s00414-021-02566-3

Deep learning in forensic gunshot wound interpretation : a proof-of-concept study

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Author: Oura, Petteri1; Junno, Alina2,3; Junno, Juho-Antti2,3,4
Organizations: 1Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
2Cancer and Translational Medicine Research Unit, University of Oulu, Oulu, Finland
3Department of Archaeology, Faculty of Humanities, University of Oulu, Oulu, Finland
4Faculty of Arts, University of Helsinki, Helsinki, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021090244984
Language: English
Published: Springer Nature, 2021
Publish Date: 2021-09-02
Description:

Abstract

While the applications of deep learning are considered revolutionary within several medical specialties, forensic applications have been scarce despite the visual nature of the field. For example, a forensic pathologist may benefit from deep learning-based tools in gunshot wound interpretation. This proof-of-concept study aimed to test the hypothesis that trained neural network architectures have potential to predict shooting distance class on the basis of a simple photograph of the gunshot wound. A dataset of 204 gunshot wound images (60 negative controls, 50 contact shots, 49 close-range shots, and 45 distant shots) was constructed on the basis of nineteen piglet carcasses fired with a .22 Long Rifle pistol. The dataset was used to train, validate, and test the ability of neural net architectures to correctly classify images on the basis of shooting distance. Deep learning was performed using the AIDeveloper open-source software. Of the explored neural network architectures, a trained multilayer perceptron based model (MLP_24_16_24) reached the highest testing accuracy of 98%. Of the testing set, the trained model was able to correctly classify all negative controls, contact shots, and close-range shots, whereas one distant shot was misclassified. Our study clearly demonstrated that in the future, forensic pathologists may benefit from deep learning-based tools in gunshot wound interpretation. With these data, we seek to provide an initial impetus for larger-scale research on deep learning approaches in forensic wound interpretation.

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Series: International journal of legal medicine
ISSN: 0937-9827
ISSN-E: 1437-1596
ISSN-L: 0937-9827
Volume: 135
Issue: 5
Pages: 2101 - 2106
DOI: 10.1007/s00414-021-02566-3
OADOI: https://oadoi.org/10.1007/s00414-021-02566-3
Type of Publication: A1 Journal article – refereed
Field of Science: 319 Forensic science and other medical sciences
3126 Surgery, anesthesiology, intensive care, radiology
217 Medical engineering
Subjects:
Funding: Open access funding provided by University of Oulu including Oulu University Hospital.
Copyright information: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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