Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

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 thickness...

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Bibliographic Details
Published in:Applied sciences 2020-10, Vol.10 (20), p.7097
Main Authors: Annala, Leevi, Ayramo, Sami, Polonen, Ilkka
Format: Article
Language:English
Subjects:
Algorithms
Computer-aided medical diagnosis
convolutional neural network
Dermis
Diagnosis
Discriminant analysis
Epidermis
Hemoglobin
Inversion
Learning algorithms
Light
Machine learning
Melanin
Melanoma
Methods
model inversion
Multilayers
Neural networks
Optical properties
Parameter estimation
physical parameter retrieval
Physical properties
Principal components analysis
Propagation
skin
Skin cancer
Stochastic models
Stochasticity
Support vector machines
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10207097