期刊
APPLIED SCIENCES-BASEL
卷 10, 期 20, 页码 -出版社
MDPI
DOI: 10.3390/app10207097
关键词
skin; physical parameter retrieval; neural networks; convolutional neural network; machine learning; model inversion
类别
资金
- Academy of Finland [314519]
- Academy of Finland (AKA) [314519, 314519] Funding Source: Academy of Finland (AKA)
Featured Application This research can potentially be applied in improving the accuracy of clinical skin cancer diagnostics. 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.
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