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Assessing Body Temperature - Axilla01:14

Assessing Body Temperature - Axilla

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Procedural Guide for Assessing Axillary Body Temperature using a Digital Thermometer:
Step 1: Perform hand hygiene and put on clean gloves to maintain infection control and prevent cross-contamination.
Step 2: Prepare the patient by explaining the procedure to ensure understanding and cooperation. Ensure privacy, expose the axilla, and inform the patient that minimal movement is crucial for an accurate reading.
Step 3: Adjust the patient’s clothing to expose only the axilla. It minimizes...
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Related Experiment Video

Updated: Jan 13, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

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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
PubMed
Summary
This summary is machine-generated.

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 cancerimage classificationmachine learningthermography

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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.