用机器学习来确定性别的数字全景射线图中下形态参数的比较
Hanife Pertek1,2, Mustafa Kamaşak3, Soner Kotan4
1Center for Nanotechnology & Biomaterials Application and Research (NBUAM), Marmara University, Istanbul, Turkey.
Oral radiology
|April 16, 2024
概括
机器学习从下巴全景放射图准确地确定性别. 结合形态特征,提高了可靠的法医和临床应用的准确性.
科学领域:
- 法医人类学 法医人类学
- 放射学 放射学是一门学科.
- 机器学习 机器学习
背景情况:
- 在法医和临床背景下,性别确定至关重要.
- 从放射图片中进行部形态测量分析提供了一个潜在的非侵入性方法.
- 机器学习算法可以分析放射数据中的复杂模式.
研究的目的:
- 为了评估机器学习算法在性别确定中的有效性,使用数字全景放射图的形态特征.
- 识别关键的下形态特征,这些特征对准确的性别分类有最大的贡献.
主要方法:
- 从200张数字全景放射图中提取了12个形态测量测量结果.
- 应用六种机器学习算法 (k-NN,决策树,SVM,naive Bayes,LDA,神经网络).
- 严格验证使用十倍交叉验证,每次分类过程重复10次.
主要成果:
- 当使用所有12个特征时,总体准确率达到了82.6%.
- 冠状体高度 (80.9%),状体高度 (78.2%) 和状体高度 (77.2%) 是最准确的个体特征.
- 纯粹的贝叶斯算法产生了最高的分类准确率84.0%.
结论:
- 机器学习有效地从全景X射线图上的下形态结构来确定性别.
- 对多个形态特征的综合分析,特别是那些由MRMR等算法选择的,最大限度地提高了分类准确性.
- 这种方法在法医和人类学研究中为性别确定提供了可靠的工具.
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