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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

Photothermal Radiometry Data Analysis by Using Machine Learning.

Perry Xiao¹, Daqing Chen¹

¹School of Engineering, London South Bank University, London SE1 0AA, UK.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

This summary is machine-generated.

Machine learning enhances photothermal techniques for biomedical and industrial uses. This study applies machine learning algorithms to opto-thermal transient emission radiometry (OTTER) for precise skin analysis.

Keywords:

classification deep learning machine learning photothermal techniques regression skin hydration

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Area of Science:

Biomedical Engineering
Artificial Intelligence
Infrared Remote Sensing

Background:

Photothermal techniques utilize infrared remote sensing for biomedical and industrial applications.
Machine learning (ML) offers data-driven analysis without explicit programming.
Opto-thermal transient emission radiometry (OTTER) is a photothermal technique for skin analysis.

Purpose of the Study:

To review recent advancements in machine learning for photothermal techniques.
To present novel machine learning applications for data analysis in OTTER.
To evaluate various ML algorithms for OTTER data interpretation.

Main Methods:

Literature review of machine learning in photothermal applications.
Implementation and comparison of regression algorithms: Random Forest, Gradient Boosting, Support Vector Machine (SVM), Partial Least Squares (PLS).
Application of deep learning neural network regression for OTTER data analysis.

Main Results:

Machine learning algorithms demonstrate effectiveness in analyzing OTTER data.
Evaluated algorithms show potential for accurate skin hydration, pigment, and topical substance penetration measurements.
Deep learning models offer advanced capabilities for complex photothermal data interpretation.

Conclusions:

Machine learning significantly improves data analysis in photothermal techniques like OTTER.
The study validates the utility of various ML regression models for biomedical skin analysis.
Future research can leverage these ML approaches for enhanced non-destructive testing and biomedical applications.