Classification of human skin cancer using Stokes-Mueller decomposition method and artificial intelligence models

Abstract A hybrid framework consisting of the Stokes- decomposition method and various artificial intelligence (AI) models is proposed for classifying human melanoma and nonmelanoma skin cancer samples. In the proposed approach, the Stokes-Mueller matrix decomposition method is first used to extract the effective optical parameters of normal, squamous cell carcinoma, basal cell carcinoma, and melanoma skin cancer samples. The parameters are pre-processing using feature scaling and dimensional reduction techniques, and are then used to train nine different AI models, namely ExtraTree, Random Forest, Decision Tree, XGBoost, Support Vector Machine, k-Nearest Neighbors (k-NN), Radius Neighbors, Multilayer Perceptron (MLP), and Ridge. Finally, the trained models are used to classify a small number of normal and cancerous skin tissue samples. It is shown that all of the models have a classification accuracy (F1 score) of more than 90% given an appropriate pre-processing of the input parameters. Furthermore, the ExtraTree, k-NN, and MLP models achieve an accuracy of 100%. Overall, the results confirm that the proposed framework provides a promising approach for the efficient and accurate classification of human skin cancer tissue samples.