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Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data.

Soumya Prakash Rana1, Maitreyee Dey1, Riccardo Loretoni2

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Summary

A new microwave device, MammoWave, combined with machine learning (ML) accurately detects breast lesions. This technology shows high accuracy, distinguishing between healthy and diseased breast tissue using frequency spectrum analysis.

Keywords:
MammoWavebreast lesion detectionmachine learning

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning in Healthcare

Background:

  • Novel microwave apparatus (MammoWave) developed for breast lesion detection.
  • MammoWave operates in air with rotating antennas (1-9 GHz band).
  • Machine learning (ML) is utilized to analyze frequency spectrum data from MammoWave.

Purpose of the Study:

  • To assess the efficacy of the MammoWave device augmented with ML for breast lesion identification.
  • To evaluate the accuracy, sensitivity, and specificity of the proposed system.

Main Methods:

  • Utilized MammoWave to record frequency spectrum from 61 breasts (35 patients).
  • Applied Principal Component Analysis (PCA) for feature extraction from frequency response.
  • Employed Support Vector Machine (SVM) with a radial basis function kernel for automated lesion identification.

Main Results:

  • MammoWave data revealed distinct frequency spectrum behaviors for tissues with and without lesions.
  • The SVM model achieved high performance metrics: 91% accuracy, 84.40% sensitivity, and 97.20% specificity.
  • Demonstrated in-vivo feasibility validated by ethical committee approvals.

Conclusions:

  • The ML-augmented MammoWave system demonstrates high accuracy in identifying breast lesions.
  • This technology offers a promising non-invasive approach for breast cancer screening.
  • Further validation and clinical integration of MammoWave are warranted.