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Radar-Based Microwave Breast Imaging Using Neurocomputational Models.

Mustafa Berkan Bicer1

  • 1Electrical and Electronics Engineering Department, Engineering Faculty, Tarsus University, 33400 Mersin, Turkey.

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|March 11, 2023
PubMed
Summary

Neurocomputational models, including deep neural networks (DNNs) and convolutional neural networks (CNNs), show promise for radar-based microwave imaging of breast tumors. Complex-valued models achieved superior accuracy in generating detailed radar images for diagnostics.

Keywords:
breast imagingcircular synthetic aperture radar (CSAR)convolutional neural networks (CNNs)deep neural networks (DNNs)inverse scattering

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Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Radar Technology

Background:

  • Breast cancer detection remains a critical challenge in women's health.
  • Radar-based microwave imaging (MWI) offers a non-ionizing imaging modality with potential for early tumor detection.
  • Developing accurate and efficient computational models is essential for advancing MWI techniques.

Purpose of the Study:

  • To propose and evaluate neurocomputational models for acquiring radar-based microwave images of breast tumors.
  • To compare the performance of real-valued and complex-valued deep learning models for MWI.
  • To assess the efficacy of these models in generating high-fidelity radar images for diagnostic purposes.

Main Methods:

  • Utilized circular synthetic aperture radar (CSAR) for MWI simulations, generating 1000 diverse scenarios with varying tumor characteristics.
  • Developed and trained four neurocomputational models: a real-valued DNN (RV-DNN), a real-valued CNN (RV-CNN), a real-valued combined U-Net model (RV-MWINet), and a complex-valued U-Net model (CV-MWINet).
  • Evaluated model performance using metrics such as Mean Squared Error (MSE), accuracy, Peak Signal-to-Noise Ratio (PSNR), Universal Quality Index (UQI), and Structural Similarity Index (SSIM).

Main Results:

  • The RV-DNN model exhibited training and test MSE of 103.400 and 96.395, respectively.
  • The RV-CNN model showed training and test MSE of 45.283 and 153.818, respectively.
  • The complex-valued CV-MWINet model achieved superior performance with training and test accuracy of 0.991 and 1.000, respectively, outperforming the real-valued RV-MWINet (0.9135 training, 0.8635 testing).

Conclusions:

  • Neurocomputational models, particularly complex-valued deep learning architectures, are effective for radar-based microwave imaging of breast tumors.
  • The developed models demonstrate the potential for accurate reconstruction of radar images, aiding in breast cancer detection.
  • Further research into complex-valued models can enhance the resolution and diagnostic capabilities of MWI systems.