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Updated: Jun 11, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient

Thanh Nguyen Chi¹, Hong Le Thi Thu², Tu Doan Quang³

¹Institute of Information Technology, AMST, Hanoi, Vietnam.

Journal of Imaging Informatics in Medicine

|October 2, 2024

Summary

This summary is machine-generated.

A new hybrid model accurately detects breast cancer using infrared thermography images. This cost-effective, non-ionizing method shows high accuracy, improving early diagnosis for women.

Keywords:

Breast cancer Breast thermal image CNN feature exactor Classifiers Feature optimization

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Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Biomedical Engineering

Background:

Breast cancer remains a leading cause of mortality in women globally.
Infrared thermography offers a cost-effective, non-ionizing radiation alternative for early detection.
Developing advanced diagnostic tools is crucial for improving patient outcomes.

Purpose of the Study:

To present a hybrid model for breast cancer detection using infrared thermography images.
To classify thermography images into healthy or cancerous categories for enhanced diagnosis.
To evaluate the performance of the proposed hybrid model against existing methods.

Main Methods:

Utilized multiple pre-trained convolutional neural networks for feature extraction from thermography images.
Employed feature filter methods, specifically the Chi-square filter, for feature selection.
Integrated diverse classifiers, including Support Vector Machine (SVM), for image classification.

Main Results:

The combination of ResNet34, Chi-square filter, and SVM classifier achieved the highest accuracy of 99.05% on the DRM-IR test set.
An accuracy improvement of 1.5% was observed with the SVM classifier and Chi-square filter compared to standard convolutional neural networks.
The proposed hybrid model demonstrated superior performance over state-of-the-art methods for breast cancer detection from thermography images.

Conclusions:

The developed hybrid model is highly accurate and computationally efficient for breast cancer detection.
This method offers a promising computer-aided diagnosis tool for early breast cancer identification.
Infrared thermography combined with advanced AI presents a viable strategy for breast cancer screening.