Photothermal Radiometry Data Analysis by Using Machine Learning
Journal article
Xiao, P. and Chen, D. (2024). Photothermal Radiometry Data Analysis by Using Machine Learning. Sensors. 24 (10), p. 3015. https://doi.org/10.3390/s24103015
Authors | Xiao, P. and Chen, D. |
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Abstract | Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new data, without being explicitly programmed to do so. In this paper, we first review the latest development of machine learning and its applications in photothermal techniques. Next, we present our latest work on machine learning for data analysis in opto-thermal transient emission radiometry (OTTER), which is a type of photothermal technique that has been extensively used in skin hydration, skin hydration depth profiles, skin pigments, as well as topically applied substances and skin penetration measurements. We have investigated different algorithms, such as random forest regression, gradient boosting regression, support vector machine (SVM) regression, and partial least squares regression, as well as deep learning neural network regression. We first introduce the theoretical background, then illustrate its applications with experimental results. |
Keywords | photothermal techniques; skin hydration; machine learning; deep learning; regression; classification |
Year | 2024 |
Journal | Sensors |
Journal citation | 24 (10), p. 3015 |
Publisher | MDPI AG |
ISSN | 1424-8220 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s24103015 |
Web address (URL) | https://www.mdpi.com/journal/sensors |
Publication dates | |
Online | 09 May 2024 |
Publication process dates | |
Accepted | 06 May 2024 |
Deposited | 09 May 2024 |
Accepted author manuscript | |
License | https://creativecommons.org/licenses/by/4.0/ |
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https://openresearch.lsbu.ac.uk/item/97204
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