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Updated: Aug 4, 2025

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Feature selection algorithm based on binary BAT algorithm and optimum path forest classifier for breast cancer

S Sasikala1, M Ezhilarasi2, S Arunkumar1

  • 1Department of ECE, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India.

Journal of Cancer Research and Therapeutics
|April 3, 2023
PubMed
Summary

This study enhances breast cancer detection using Local Binary Pattern (LBP) texture features from ultrasound images. The LBP method significantly improves accuracy and reduces false negatives, aiding in earlier and more reliable diagnosis.

Keywords:
Breast cancerbinary bat algorithmechographyelastographyoptimum path forest classifiersupport vector machine

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Breast cancer incidence and mortality are increasing globally.
  • Mammography has limitations, especially in dense tissues, leading to missed diagnoses.
  • Sonography offers supplementary information for breast cancer detection.

Purpose of the Study:

  • To improve breast cancer detection performance by reducing false positives and false negatives.
  • To evaluate the efficacy of Local Binary Pattern (LBP) texture features in ultrasound imaging for breast cancer diagnosis.

Main Methods:

  • Extracted Local Binary Pattern (LBP) texture features from elastographic and echographic ultrasound images.
  • Employed a hybrid feature selection technique (binary BAT algorithm and optimum path forest) for feature reduction.
  • Fused features and utilized a support vector machine classifier for final classification.

Main Results:

  • Local Binary Pattern (LBP) features achieved 93.2% accuracy, 94.4% sensitivity, and 92.3% specificity.
  • LBP outperformed other texture features like GLCM, GLDM, and LAWs.
  • Achieved a high Mathews correlation coefficient of 0.861.

Conclusions:

  • The proposed LBP-based method demonstrates superior performance in breast cancer detection.
  • The enhanced specificity of this method is crucial for minimizing false negatives.
  • This approach holds promise for more accurate and reliable breast cancer diagnosis.