根据VGG16模型的卷积神经网络方法进行法医性别分类:准确性,精度和灵敏度
Cristiana Palmela Pereira1,2,3, Mariana Correia4, Diana Augusto4
1Centro de Estatística e Aplicações Universidade de Lisbao, CEAUL, Faculdade de Ciências da Universidade de Lisboa no Bloco C6 - Piso 4, Lisboa, 1749-016, Portugal. cpereira@campus.ul.pt.
International journal of legal medicine
|January 24, 2025
概括
人工智能,特别是卷积神经网络 (CNN),提供了一种可靠的方法,用于从骨科手术 (OPG) 进行法医性别估计. 这种人工智能方法在从牙部图像中识别性别时实现了89%的准确性,有助于医疗法律识别.
科学领域:
- 法医人类学 法医人类学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 性别估计对于医疗法律识别中的生物概况重建至关重要.
- 传统的性别估计方法可能是主观的.
- 卷积神经网络 (CNN) 为性别估计提供了一个客观的替代方案.
研究的目的:
- 评估VGG16模型在法医性预测中的可靠性.
- 为了评估VGG16的性能,使用骨科术 (OPG).
主要方法:
- 使用了来自牙科部门的1050个OPG.
- 使用Python进行预处理和增强的OPG图像.
- 使用精度,灵敏度,F1分数和准确度评估模型性能,生成热图.
主要成果:
- VGG16模型在性别分类方面实现了89%的整体准确性.
- 该模型表现出性别之间的平衡表现,F1得分为0.89.
- 在16-20岁的年龄组中观察到最高的准确性 (90%);热图显示重点在非解剖学领域.
结论:
- 在使用OPGs的医疗法律识别中,CNN对于性别分类是准确的.
- VGG16模型显示出潜力,但需要进一步研究以提高性能.
- 未来的工作应该包括图像提取技术,以改善对相关解剖区域的关注.
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