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Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver.

Ahmad Ashraf Abdul Halim1,2, Allan Melvin Andrew1,2, Wan Azani Mustafa3,4

  • 1Advanced Communication Engineering (ACE), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), No. 15 & 17, Jalan Tiga, Pengkalan Jaya Business Centre, Kangar 01000, Perlis, Malaysia.

Diagnostics (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

Early breast cancer detection is improved with a new Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) method. This intelligent classifier achieves 96.3% accuracy, aiding in timely diagnosis and treatment.

Keywords:
breast cancerfeature engineeringfeature selectionmachine learningmulti-stagepredictionsupervised learning

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning

Background:

  • Breast cancer is a leading cause of death in women, often due to late detection.
  • Early diagnosis is critical for effective treatment and improved survival rates.
  • Current detection methods face challenges in identifying early-stage tumors.

Purpose of the Study:

  • To develop a robust intelligent classifier for early breast cancer detection.
  • To enhance diagnostic accuracy using statistical feature analysis.
  • To investigate the efficacy of Ultra-Wideband (UWB) imaging combined with advanced feature selection.

Main Methods:

  • A novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) framework was proposed.
  • 39,000 data samples of breast tissue with varying tumor sizes were generated and imaged using UWB signals.
  • Feature selection and extraction were performed, prioritizing frequency domain data and utilizing Analysis of Variance (ANOVA).

Main Results:

  • The MSFS-BPSO method demonstrated high classification accuracy, reaching up to 96.3%.
  • The study confirmed the superiority of frequency domain features over time domain features for classification.
  • The Probabilistic Neural Network (PNN) classifier with Binary Particle Swarm Optimization (BPSO) identified the optimal feature subset.

Conclusions:

  • The MSFS-BPSO method offers a dependable and accurate approach for early breast cancer detection.
  • This intelligent classifier shows significant potential for improving diagnostic outcomes in women.
  • The integration of UWB imaging and advanced machine learning techniques provides a promising direction for breast cancer diagnostics.