Skin cancer detection using effective optical parameters and the classification and regression tree algorithm: A novel framework

Abstract

Early detection of skin cancer matters because diagnosis, prognosis and treatment plan differ for each skin cancer type at their stages. Medical imaging taking the advantages of the non-invasive and non-ionizing polarized light is emerging as a tool for the development of screening and diagnotic tests. In this work, we proposed a novel framework to classify human melanoma and nonmelanoma skin cancer using the Classification and Regression Tree algorithm (CART). The samples were prepared from twenty-four non-melanoma skin cancer samples (consisting of twelve squamous cell carcinoma and twelve basal cell carcinoma samples); and three melanoma skin cancer samples. We calculated ten optical parameters from anisotropic biological tissues, namely the LB orientation angle (α), the LB phase retardance (β), the CB optical rotation angle (γ), the LD orientation angle (θd), the linear dichroism (D), the circular dichroism (R), the degrees of linear depolarization (e1 and e2), the degree of circular depolarization (e3), and the depolarization index (∆) using Stokes-Mueller matrix formalism. All effective optical parameters of biological tissue were then input into the CART classifier as predictors. The model yielded an accuracy of 92.6%, which is desirable for any robust and interpretable classification model. The results showed that for biological tissue samples, linear polarization properties dominate over circular ones due to the cellular microstructural composition of tissue, especially under anomalous growth as seen in skin cancer. This novel framework can potentially assist physicians in making timely and well-informed medical decisions.