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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography.

Roger Resmini1,2, Lincoln Silva2,3, Adriel S Araujo2

  • 1Institute of Exact and Natural Sciences, Federal University of Rondonópolis, Cidade Universitária, Rondonópolis 78736-900, MT, Brazil.

Sensors (Basel, Switzerland)
|July 24, 2021
PubMed
Summary

This study introduces a novel ensemble method using a Genetic Algorithm (GA) and Support Vector Machine (SVM) for early breast cancer diagnosis. The approach significantly improves detection accuracy, aiding in increased survival rates.

Keywords:
breast cancerdiagnosisgenetic algorithmsupport vector machinethermography

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning in Healthcare

Background:

  • Breast cancer remains a leading global cause of mortality.
  • Early diagnosis is crucial for improving patient survival rates.
  • Thermography offers a non-invasive method for detecting temperature variations indicative of breast cancer.

Purpose of the Study:

  • To develop and evaluate an ensemble method for breast cancer diagnosis.
  • To combine Genetic Algorithm (GA) for feature selection and Support Vector Machine (SVM) for classification.
  • To enhance the accuracy and reliability of early breast cancer detection using thermographic data.

Main Methods:

  • An ensemble approach integrating a Genetic Algorithm (GA) and Support Vector Machine (SVM) classifier was developed.
  • The GA was employed for optimal feature selection from thermographic data.
  • The SVM classifier was utilized to diagnose breast cancer based on selected features.

Main Results:

  • The proposed ensemble method achieved high diagnostic performance.
  • Achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 94.79%.
  • Demonstrated an overall accuracy of 97.18% in breast cancer diagnosis.

Conclusions:

  • The developed GA-SVM ensemble method is a significant contribution to early breast cancer diagnosis.
  • The approach shows promise for improving early detection rates and patient outcomes.
  • Thermography combined with advanced machine learning techniques offers a viable tool for breast cancer screening.