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Related Experiment Video

Updated: Sep 11, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection.

Marwa H Sharaf1,2, Manuel Arrebola3, Khalid F A Hussein4

  • 1Electronics and Communications Department, College of Engineering and Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria 21937, Egypt.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a novel deep learning framework using microwave radar for non-invasive breast cancer detection. The system accurately estimates tumor position, size, and depth, offering a promising advancement in early diagnosis.

Keywords:
antenna arraysantenna designbreast cancer detectiondeep learningdielectric propertiesmicrowave imagingmicrowave radar systemneural networksspecific absorption rate (SAR)ultra-wideband antenna

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning

Background:

  • Accurate and non-invasive breast cancer detection is crucial for effective treatment.
  • Current methods face challenges in sensitivity and specificity.
  • Microwave imaging offers potential for safe and early tumor detection.

Purpose of the Study:

  • To develop a deep learning framework for non-invasive breast cancer detection using microwave radar.
  • To design and validate an ultra-wideband antenna for enhanced tumor detection sensitivity.
  • To create an innovative Attention-Based Feature Separation (ABFS) model for precise tumor parameter estimation.

Main Methods:

  • Evolutionary design and experimental validation of an arc-shaped, ultra-wideband octagram ring patch antenna.
  • Assessment of Specific Absorption Rate (SAR) distributions and power adjustment for safety compliance.
  • Development of the Attention-Based Feature Separation (ABFS) deep learning model using simulated S-parameters.
  • Multi-branch neural network for tumor localization and size estimation.

Main Results:

  • The designed antenna demonstrated effective signal propagation and interaction with breast tissue.
  • The ABFS model dynamically identified optimal frequency sub-bands and disentangled discriminative features.
  • The deep learning framework achieved high estimation accuracy and computational efficiency in simulations.
  • The proposed approach showed superior prediction accuracy and interpretability compared to conventional attention mechanisms.

Conclusions:

  • The integration of deep learning with conformal microwave imaging provides a safe, effective, and non-invasive method for breast cancer detection.
  • The ABFS model shows significant promise for improving the accuracy and interpretability of tumor parameter estimation.
  • This framework represents a significant advancement in diagnostic tools for breast cancer.