A1 Journal article (refereed)
Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion (2020)
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
JYU authors or editors
Publication details
All authors or editors: Annala, Leevi; Äyrämö, Sami; Pölönen, Ilkka
Journal or series: Applied Sciences
eISSN: 2076-3417
Publication year: 2020
Volume: 10
Issue number: 20
Article number: 7097
Publisher: MDPI AG
Publication country: Switzerland
Publication language: English
DOI: https://doi.org/10.3390/app10207097
Open Access: Publication published in an open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72408
Abstract
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.
Keywords: spectral imaging; imaging; machine learning; neural networks (information technology); skin cancer; diagnostics
Free keywords: skin; physical parameter retrieval; convolutional neural network; model inversion
Contributing organizations
Related projects
- SPECTRAL IMAGING OF COMPLEX SURFACE TOMOGRAPHIES
- Pölönen, Ilkka
- Academy of Finland
Ministry reporting: Yes
Preliminary JUFO rating: 1