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Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

Anas Bilal1,2, Azhar Imran3, Talha Imtiaz Baig4

  • 1College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.

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This study introduces a new hybrid method for early breast cancer detection, significantly improving classification accuracy. The novel approach enhances diagnostic performance for mammography analysis.

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Area of Science:

  • * Medical Imaging
  • * Artificial Intelligence
  • * Computational Biology

Background:

  • * Early breast cancer detection is crucial for effective treatment.
  • * Computer-Aided Diagnosis (CAD) systems aid in mammography analysis but have accuracy limitations.
  • * Existing optimization algorithms like Particle Swarm Optimization and Genetic Algorithm show suboptimal performance in breast cancer classification.

Purpose of the Study:

  • * To enhance breast cancer classification accuracy by optimizing Support Vector Machine (SVM) parameters.
  • * To introduce a novel hybrid approach combining an improved quantum-inspired binary Grey Wolf Optimizer (IQI-BGWO) with SVM Radial Basis Function Kernel.
  • * To address the limitations of existing CAD systems in achieving optimal accuracy for breast cancer detection.

Main Methods:

  • * Hybridization of the improved quantum-inspired binary Grey Wolf Optimizer (IQI-BGWO) with Support Vector Machines Radial Basis Function Kernel.
  • * Evaluation of the proposed IQI-BGWO-SVM approach on the Mammography Image Analysis Society (MIAS) dataset.
  • * Application of IQI-BGWO-SVM for feature selection and comparison with existing methods.
  • * Utilization of tenfold cross-validation for performance assessment.

Main Results:

  • * The IQI-BGWO-SVM technique achieved superior performance compared to state-of-the-art methods on the MIAS dataset.
  • * Mean accuracy, sensitivity, and specificity were reported as 99.25%, 98.96%, and 100%, respectively.
  • * The hybrid approach demonstrated effectiveness in both breast cancer classification and feature selection.

Conclusions:

  • * The proposed IQI-BGWO-SVM approach significantly improves breast cancer classification accuracy.
  • * This hybrid method offers a promising advancement for early breast cancer detection using automated mammography.
  • * The study highlights the potential of quantum-inspired optimization techniques in medical image analysis.