Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier. Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

中文翻译：

---

利用随机森林算法对人类皮肤癌进行分类的 Mueller 矩阵元素的表征

意义：穆勒矩阵分解法广泛用于生物样品的分析。然而，它假定的基本光学效应（例如，二色性、延迟和去偏振）的连续出现限制了它的准确性和应用。目的：基于Mueller矩阵元素的特征和随机森林（RF）算法，提出了一种检测和分类人类黑色素瘤和非黑色素瘤皮肤癌病变的方法。方法：在提议技术中，对应于从具有鳞状细胞癌 (SCC)、基底细胞癌 (BCC)、黑色素瘤和正常特征的 32 个组织样本中获得的 Mueller 矩阵的 16 个元素的 669 个数据点被输入到 RF 分类器中作为预测器。结果：结果表明，所提出的模型产生了 93% 的平均精度。此外，分类结果表明，对于生物组织，圆极化特性（即Mueller矩阵的元素m44、m34、m24和m14）主导线极化特性（即Mueller矩阵的元素m13、m31、m22和m41）。矩阵）来确定训练分类器的分类结果。结论：总体而言，我们的研究为开发人类皮肤癌的分类和诊断技术提供了一种简单、准确且具有成本效益的解决方案。