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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

A method for improving semantic segmentation using thermographic images in infants.

Hidetsugu Asano¹, Eiji Hirakawa^2,3, Hayato Hayashi⁴

¹Technical Department, Atom Medical Corporation, 2-2-1, Dojo, Sakura-ku, Saitama city, Saitama, 338-0835, Japan. hidetsugu.asano@atomed.co.jp.

BMC Medical Imaging

|January 4, 2022

Summary

This study introduces an advanced AI method for contactless temperature monitoring in neonates using thermal imaging. The U-Net GAN with Self-Attention significantly improved segmentation accuracy for continuous whole-body temperature assessment.

Keywords:

Infants Semantic segmentation Temperature Thermography

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Area of Science:

Medical Imaging
Artificial Intelligence
Neonatal Care

Background:

Accurate temperature regulation is critical for neonatal outcomes.
Traditional methods like skin probes offer limited temperature distribution data.
Existing thermographic techniques require manual region selection, hindering clinical use.

Purpose of the Study:

To develop an automated method for continuous, contactless whole-body temperature monitoring in neonates.
To evaluate the efficacy of deep learning-based semantic segmentation for neonatal thermal imaging.
To enhance the precision of temperature distribution analysis in clinical settings.

Main Methods:

Applied the U-Net semantic segmentation model to neonatal thermal images.
Evaluated combinations of Weight Normalization, Group Normalization, and Flexible Rectified Linear Unit (FReLU).
Integrated U-Net Generative Adversarial Network (U-Net GAN) and a Self-Attention (SA) module for improved segmentation.

Main Results:

U-Net with FReLU and Group Normalization achieved 92.9% accuracy and 64.5% mIoU.
U-Net GAN improved performance to 93.3% accuracy and 66.9% mIoU.
U-Net GAN + SA further enhanced results to 93.5% accuracy and 70.4% mIoU.

Conclusions:

FReLU and Group Normalization are suitable for neonatal thermal image segmentation.
U-Net GAN and U-Net GAN + SA significantly improved segmentation accuracy (mIoU).
This AI-driven approach enables precise, continuous contactless temperature monitoring in neonates.