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通过表面纹理分析和SVM建模区分良性和恶性口腔病变

Barış Oğuz Gürses¹, Nezaket Ezgi Özer², Gaye Bölükbaşı²

¹Department of Mechanical Engineering, Faculty of Engineering, Ege University, Bornova, Izmir, Türkiye.

Clinical oral investigations

|September 2, 2025

概括

使用统计特征对口腔病变的表面纹理分析可以客观地区分良性和恶性类型. 一个支持矢量机 (SVM) 模型在分类这些病变方面表现出很高的准确性.

科学领域:

生物医学成像分析
计算病理学
口腔瘤诊断

背景情况:

口腔粘膜病变需要精确区分良性和恶性类型才能有效治疗.
需要客观的诊断参数来补充主观的临床评估.

研究的目的:

从临床图像中评估表面纹理特征的诊断潜力,以区分良性和恶性口腔病变.
使用这些纹理特征验证支持矢量机 (SVM) 模型的分类性能.

主要方法:

从275张口内粘膜病变照片中提取了基于灰度的纹理特征.
计算了八个统计描述符 (例如,,曲率,变化系数) 并将它们规范化.
使用五倍交叉验证进行损伤分类.

主要成果:

在良性病变和恶性病变之间发现了显著的纹理特征 (除斜率外) (p < 0. 001).
和形表现出最大的效果大小,区分病变类型.
SVM模型的整体精度为90. 5%,灵敏度为99. 2%,特异性为81. 4%,AUC为0. 939.

结论:

表面纹理特征,特别是和,是有希望的客观指标,用于区分恶性和良性口腔病变.
SVM分类器表现出强大的性能,突出显示了将表面拓学整合到基于人工智能的口腔癌查诊断工具中的潜力.

关键词:

计算机辅助的诊断计算机辅助的图像处理机器学习口腔新生体表面质地

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