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Technical note: The impact of image size on bloodstain pattern analysis using machine learning.

Ainaz Alavi1, Theresa Stotesbury2, Peter R Lewis3

  • 1Faculty of Business and Information Technology, Ontario Tech University, 2000 Simcoe St N, Oshawa, L1G 0C5, Ontario, Canada; Faculty of Science, Forensic Science Ontario Tech University, 2000 Simcoe St N, Oshawa, L1G 0C5, Ontario, Canada.

Forensic Science International
|November 16, 2025
PubMed
Summary

High-resolution images are not essential for accurate bloodstain pattern classification using machine learning (ML). Simplified models with fewer features also achieve comparable accuracy, suggesting more efficient forensic image analysis is possible.

Keywords:
Bloodstain pattern analysisEffect of image resolutionHigh vs low-resolutionImage resolutionMachine learning in forensic sciencePattern recognitionRandom forest classifier

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Area of Science:

  • Forensic Science
  • Computer Vision
  • Machine Learning

Background:

  • Bloodstain pattern analysis (BPA) is crucial in forensics.
  • High-resolution imaging and complex machine learning (ML) models are computationally demanding.
  • The necessity of high-resolution images for ML-based BPA accuracy is questioned.

Purpose of the Study:

  • To investigate the impact of image resolution on ML-based bloodstain pattern classification.
  • To evaluate the effectiveness of feature selection in simplifying ML models for BPA.
  • To explore the potential for more efficient and accurate forensic image analysis.

Main Methods:

  • Replication of an existing experiment distinguishing impact vs. forward spatter.
  • Comparison of classification accuracy using original and resized (reduced resolution) images.
  • Assessment of classification performance with reduced feature sets (4 features vs. 58 features).

Main Results:

  • Reducing image resolution did not significantly impact classification accuracy.
  • A model using only four key features achieved accuracy comparable to the original model with 58 features.
  • ML models demonstrated robustness to reduced image detail and complexity.

Conclusions:

  • High-resolution images may not be necessary for accurate ML-based bloodstain pattern classification.
  • Feature selection can lead to simpler, more efficient ML models without compromising accuracy.
  • Understanding ML model behavior is critical for responsible implementation in forensic workflows.