Abstract
.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 , , , and of the Mueller matrix) dominate the linear polarization properties (i.e., elements , , , and 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.
Highlights
According to the International Agency for Research on Cancer, there were 300,000 new cases of melanoma and over 1,000,000 new cases of non-melanoma skin cancer in 2018.1,2 the true number of skin cancer cases may be even higher than this figure due to many factors such as the registration methodology of skin cancer, the quality of skin cancer data.[3]
The classification accuracy is equal to 100% in virtually every fold
The optimal classification performance was obtained for the melanoma class, with 30 true positive cases, no false positive case or false negative case
Summary
According to the International Agency for Research on Cancer, there were 300,000 new cases of melanoma and over 1,000,000 new cases of non-melanoma skin cancer in 2018.1,2 the true number of skin cancer cases may be even higher than this figure due to many factors such as the registration methodology of skin cancer, the quality of skin cancer data.[3] Without early detection and preventative control, melanoma can quickly develop and become far riskier, e.g., from stage I with a five-year survival rate of 97% to stage IV with a five-year survival rate of just 20% to 10%.3. The early detection of skin cancer is essential in improving the prognosis of skin cancer patients. Luu et al.: Characterization of Mueller matrix elements for classifying human skin. Current diagnostic methods for skin lesions are subjective and imprecise.
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