在人类皮肤上获得的空间分辨的分散反射和自光谱的基于机器学习的分类,用于诊断行为性角质瘤和皮肤癌的皮肤癌症,以帮助诊断
Valentin Kupriyanov1,2, Walter Blondel1, Christian Daul1
1Université de Lorraine, CNRS, CRAN UMR, Vandoeuvre-Lès-Nancy, France.
Journal of biomedical optics
|March 5, 2025
概括
这项研究表明,光学光谱学与机器学习相结合,可以帮助诊断皮肤癌,如基底细胞癌 (BCC) 和状细胞癌 (SCC). 多模式方法提高了早期检测角质细胞癌 (KCs) 的准确性.
科学领域:
- 皮肤病学 皮肤病学
- 生物医学工程 生物医学工程
- 光学物理学的光学物理学
背景情况:
- 在全球范围内,角质细胞癌 (KC) 的发病率正在上升.
- 早期诊断KC对于有效治疗和降低医疗保健成本至关重要.
研究的目的:
- 评估光学光谱仪的诊断性能与机器学习相结合,用于检测角质细胞癌 (KC).
- 为了比较不同的机器学习算法来分类皮肤病变.
主要方法:
- 收集了来自131名体内患者的自光和扩散反射光谱.
- 使用支向量机,分辨分析和多层感知子进行分类.
- 在基底细胞癌 (BCC),状细胞癌 (SCC),行动性角质瘤 (AK) 和健康皮肤的二进制和多类模式中评估性能.
主要成果:
- 对BCC/SCC与健康皮肤的二元分类准确度很高.
- 行动性角质炎 (AK) 分类的准确性在65%至75%之间.
- 多类分类达到了57%的准确性,决策融合提高了结果高达10个百分点.
结论:
- 使用机器学习的光学光谱学显示了KC诊断的前景.
- 取得的分类准确度与全科医生查可比.
- 多模态光谱对单模态方法具有优势,可以改进诊断辅助.
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