基于深度学习的人类枪伤分类
Renato Queiroz Nogueira Lira1, Luana Geovana Motta de Sousa2, Maisa Luana Memoria Pinho2
1Department of Dentistry, University of Brasília, Campus Darcy Ribeiro, Asa Norte, Brasília, 70910-900, Distrito Federal, Brazil. renato.lira@aluno.unb.br.
International journal of legal medicine
|November 6, 2024
概括
深度学习模型可以分类枪伤模式,区分入口和出口伤口,并确定射击距离. ResNet152表现出强的性能,通过人工智能帮助法医病理学.
科学领域:
- 法医科学 法医科学 法医科学
- 计算机科学 计算机科学
- 病理学 病理学 病理学
背景情况:
- 深度学习 (DL) 已经改变了医学图像分析,但在法医病理学中未得到充分利用.
- 对于法医调查来说,分类枪伤模式至关重要,但通常依赖于主观的人类分析.
- 法医证据的视觉性质使其适合于人工智能驱动的模式识别.
研究的目的:
- 调查深度学习 (DL) 对从数字图像中分类枪伤模式的有效性.
- 为了区分入口和出口伤口,使用DL.
- 通过DL分析来确定医疗法律射击距离 (MLSD) 类别 (接触,近距离,远距离).
主要方法:
- 一个全面的数据库2551犯罪现场图像的枪伤被策划.
- 评估了59个DL架构,用于伤口分类和MLSD确定.
- 专门分析了ResNet152架构的性能指标.
主要成果:
- 在入口/出口伤口分类方面,ResNet152实现了86.90%的准确性和82.09%的AUC.
- 在MLSD分类方面,ResNet152的准确率为92.48%,AUC高达94.36%.
- 由于捕捉条件的图像变化带来了挑战,反映了现实世界的法医场景.
结论:
- 深度学习,特别是ResNet152架构,显示了对分类枪伤模式的巨大潜力.
- 人工智能可以作为一个有价值的工具来支持法医病理学家,提高准确性和效率.
- 需要进一步开发以应对图像标准化挑战,以便在法医病理学中更广泛地应用人工智能.
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