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An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications.

Michele Ambrosanio1, Stefano Franceschini2, Vito Pascazio2

  • 1Dipartimento di Scienze Motorie e del Benessere, University of Napoli Parthenope, Via Medina 40, 80133 Napoli, Italy.

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Summary
This summary is machine-generated.

Artificial neural networks offer a promising approach for real-time quantitative microwave breast imaging. This method demonstrates robustness against noise and potential as a cost-effective, safe, and fast alternative to conventional techniques.

Keywords:
artificial intelligencebreast imagingelectromagnetic inverse scatteringmicrowave tomographyneural networks

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Developing effective and real-time quantitative microwave breast imaging is crucial for diagnostic applications.
  • Artificial neural networks (ANNs) are explored for their potential in this field.
  • Optimization of network architecture and performance is key.

Purpose of the Study:

  • To propose and analyze an ANN approach for quantitative microwave breast imaging.
  • To optimize network architecture for improved recovery performance and processing time.
  • To evaluate the feasibility of ANNs in a breast imaging framework.

Main Methods:

  • Generation of a suitable database for training neural networks.
  • Design and analysis of various neural network architectures.
  • Numerical testing in noisy scenarios with varying signal-to-noise ratios (SNR).

Main Results:

  • The proposed ANN methodology demonstrated good robustness against noise.
  • Promising qualitative and quantitative results were achieved compared to conventional methods.
  • Effective real-time processing capabilities were highlighted.

Conclusions:

  • Quantitative microwave imaging combined with ANNs offers a viable alternative or complement to current medical imaging.
  • This approach is cheaper, safer, faster, and provides quantitative data.
  • It has the potential to assist in medical decision-making.