Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

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.

Polarimetric biomarkers of peri-tumoral stroma can correlate with 5-year survival in patients with left-sided colorectal cancer

“…However, multiple studies highlighting inadequate performance of most of these tests, along with their high costs 9 , 10 , drive the search for alternative sources of prognostic information. In this context, increasing evidence supports the prognostic value contained within the tumour micro-environment, such as tumour stromal architecture 11 – 15 , more specifically desmoplasia or the desmoplastic response (DR) 16 – 19 . DR is associated with the growth and structural remodeling of collagenous stroma in the most invasive tumour front regions.…”

“…However, these properties may not directly highlight the underlying structure and arrangement of collagen within the tissue sample, and thus much work has gone into interpreting and correlating the observed polarimetric parameter changes with their core biological or biophysical meaning. Yet with the advancement of artificial intelligence (AI) in recent years, an alternative to this detailed understanding/interpretation has emerged whereby researchers have used various machine and deep learning techniques to directly link up the rich biological information from the polarization properties with clinical diagnosis and prognosis 15 , 27 – 33 .…”

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

“…The latter could be utilized to mimic human-like intellect when handling large and complex datasets, images, etc. Being part of AI, the vastly expanding field of machine learning (ML) covers a wide spectrum of applications for solving multiple scientific problems [47][48][49][50][51][52][53] as well as for cancer classification [54][55][56][57][58][59][60][61][62]. Since conventional programming processes an input data by means of particular syntax and semantics to produce a desired output, such a method is prone to multiple errors repetition.…”

Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection

“… 31 , 32 Recently, the same group developed a polarization technique for dengue virus detection 33 and skin cancer detection using deep learning techniques based on polarization properties. 34 , 35 In this study, a combination of MMIP and AI classification framework was utilized to perform HBV detection in human blood serum samples in the reflectance configuration. The MMIP technique was first employed to extract Mueller matrix images of 274 blood serum samples, comprising 138 HBsAg-containing (positive) samples and 136 HBsAg-free (negative) samples, respectively.…”

“…It is noted that the proposed approach in this study based on polarimetry imaging in reflectance configuration provides more versatile information than that based on an absolute value from one single point of the previous studies. 34 , 35 Furthermore, it is more useful for the development of classification algorithms and noninvasive techniques for biosensing applications.…”