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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

Lightweight Statistical and Texture Feature Approach for Breast Thermogram Analysis.

Ana P Romero-Carmona¹, Jose J Rangel-Magdaleno¹, Francisco J Renero-Carrillo¹

¹Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) Tonantzintla, Puebla 72840, Mexico.

Journal of Imaging

|October 28, 2025

Summary

Thermography, a breast cancer detection method, uses skin temperature to identify anomalies. A machine learning model achieved 91.97% accuracy classifying thermograms, aiding early breast cancer detection.

Keywords:

breast cancer image classification machine learning thermography

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Area of Science:

Oncology
Medical Imaging
Machine Learning

Background:

Breast cancer is a leading global cancer diagnosis in women.
Early detection is crucial for improving patient outcomes.
Thermography offers a non-invasive method to detect thermal anomalies associated with breast cancer.

Purpose of the Study:

To develop a concise classification tool for thermographic images.
To accurately classify thermograms as normal or indicative of potential breast cancer.
To simplify the interpretation of thermographic data for medical professionals.

Main Methods:

Utilized statistical and texture features for image analysis.
Employed a Coarse Decision Tree (DT) classifier.
Developed a robust Machine Learning (ML) model for classification.

Main Results:

Achieved a maximum classification accuracy of 91.97%.
Identified a concise set of seven features for effective classification.
Demonstrated competitive performance against existing studies.

Conclusions:

A concise feature set combined with a Decision Tree classifier provides accurate breast cancer risk assessment from thermography.
This approach facilitates easier interpretation of results for clinicians and patients.
Thermography shows promise as a complementary tool for early breast cancer detection.