• From images to detection: Mach...

Treffer: From images to detection: Machine learning for blood pattern classification.

Title:
From images to detection: Machine learning for blood pattern classification.
Authors:
Li Y; University of California, Davis, United States of America. Electronic address: ilnli@ucdavis.edu., Shen W; University of California, Irvine, United States of America. Electronic address: weinings@uci.edu.
Source:
Forensic science international [Forensic Sci Int] 2025 Oct; Vol. 375, pp. 112558. Date of Electronic Publication: 2025 Jul 13.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Science Ireland Country of Publication: Ireland NLM ID: 7902034 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-6283 (Electronic) Linking ISSN: 03790738 NLM ISO Abbreviation: Forensic Sci Int Subsets: MEDLINE
Imprint Name(s):
Publication: Limerick : Elsevier Science Ireland
Original Publication: Lausanne, Elsevier Sequoia.
MeSH Terms:
Forensic Medicine*/methods , Blood Stains* , Image Processing, Computer-Assisted*/methods , Pattern Recognition, Automated* , Boosting Machine Learning Algorithms*, Crime Victims ; Humans
Contributed Indexing:
Keywords: Bloodstain pattern analysis; Feature extraction; Forensic statistics; Image processing; Random forest; XGBoost
Entry Date(s):
Date Created: 20250715 Date Completed: 20250909 Latest Revision: 20250909
Update Code:
20250910
DOI:
10.1016/j.forsciint.2025.112558
PMID:
40664031
Database:
MEDLINE

Weitere Informationen

Bloodstain Pattern Analysis (BPA) helps us understand how bloodstains form, with a focus on their size, shape, and distributions. This aids in crime scene reconstruction and provides insight into victim positions and crime investigation. One challenge in BPA is distinguishing between different types of bloodstains, such as those from firearms, impacts, or other mechanisms. Our study focuses on differentiating impact spatter bloodstain patterns from gunshot backward spatter bloodstain patterns. We distinguish patterns by extracting well-designed individual stain features, applying effective data consolidation methods, and selecting boosting classifiers. As a result, our model exhibits competitive accuracy and efficiency on the tested dataset, suggesting its potential in similar scenarios.
(Copyright © 2025 Elsevier B.V. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.