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Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems.

Pennipat Nabheerong1, Warissara Kiththiworaphongkich2, Watcharaporn Cholamjiak3

  • 1Radiology Department, School of Medicine, University of Phayao, Phayao 56000, Thailand.

International Journal of Breast Cancer
|September 18, 2023
PubMed
Summary

This study introduces a novel algorithm for breast cancer detection in mammography, enhancing extreme learning machines. The new method shows superior performance compared to existing machine learning models.

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Optimization Algorithms

Background:

  • Early breast cancer detection is crucial for effective treatment.
  • Mammography screening is a primary tool for early detection.
  • Optimizing machine learning models can improve diagnostic accuracy.

Purpose of the Study:

  • To develop and validate a modified optimization algorithm for extreme learning machines (ELMs) in breast cancer detection.
  • To improve the accuracy and efficiency of breast cancer detection using mammography.
  • To demonstrate the superiority of the proposed algorithm over existing methods.

Main Methods:

  • Modification of the inertial relaxed CQ algorithm with Mann's iteration for split feasibility problems.
  • Application of the modified algorithm as an optimizer within an extreme learning machine framework.
  • Rigorous mathematical proof of the weak convergence of the proposed algorithm under mild conditions.

Main Results:

  • The proposed algorithm achieved high performance metrics: 85.03% accuracy, 82.56% precision, 87.65% recall, and 85.03% F1-score.
  • Comparative analysis demonstrated that the new algorithm outperforms other machine learning models in breast cancer detection.
  • The algorithm's effectiveness was validated for mammography screening applications.

Conclusions:

  • The modified inertial relaxed CQ algorithm offers a significant advancement for ELM-based breast cancer detection.
  • This optimization technique enhances diagnostic performance in mammography screening.
  • The study highlights the potential of advanced optimization methods in improving medical imaging analysis.