Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion
Annala, Leevi; Äyrämö, Sami; Pölönen, Ilkka (2020). Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion. Applied Sciences, 10 (20), 7097. DOI: 10.3390/app10207097
Published inApplied Sciences
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer perceptron, lasso, stochastic gradient descent, and linear support vector machine regressors, we find the convolutional neural network to be the most accurate in the inversion task.