. 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.

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Accurate brain tumour segmentation plays a key role in cancer diagnosis, treatment planning, and treatment evaluation. Since the manual segmentation of brain tumours is laborious, the development of semi-automatic or automatic brain tumour segmentation methods makes enormous demands on researchers [1]. Ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) acquisition protocols are standard image modalities that are used clinically. Many previous studies have shown that the multimodal MRI protocols can be used to identify brain tumours for treatment strategy, as the different image contrasts of these MRI protocols can be used to extract important complementary information. The multimodal MRI protocols include T2-weighted fluidattenuated inversion recovery (FLAIR), T1-weighted (T1), T1-weighted contrast-enhanced (T1c) and T2-weighted (T2). In recent years, an annual workshop and challenge, called Multimodal Brain Tumour Image Segmentation (BRATS), is held to different benchmark methods that have been developed to segment the brain tumour [2]. The previous studies on brain tumour segmentation can be categorised into unsupervised learning [3] and supervised learning [4, 5] methods. We only reviewed some of the most recent and closely relevant studies to our method. Unsupervised learning-based clustering has been successfully applied for the brain tumour segmentation.