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Microwave-based breast cancer detection using a high-gain Vivaldi antenna and metasurface neural network approach for medical diagnostics

  • Rasool M. Al-Gburi , Mohammad Alibakhshikenari ORCID logo EMAIL logo , Bal S. Virdee ORCID logo , Teba M. Hameed , Dion Mariyanayagam , Sandra Fernando , Innocent Lubangakene , Yi Tang , Salah Uddin Khan and Taha A. Elwi EMAIL logo
Published/Copyright: April 25, 2025
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Frequenz
From the journal Frequenz Volume 79 Issue 7-8

Abstract

This paper presents a novel technique for detecting tumors in human breasts using a single high-gain antenna and a metasurface (MTS) layer. An artificial neural network (ANN) is employed to classify detected tumors as benign or malignant based on the permittivity of the tissue. The detection and classification process leverages the contrast in dielectric properties between normal and abnormal biological tissue, utilizing the actual permittivity as a distinguishing factor. This study highlights the effectiveness of the proposed technique in accurately detecting and localizing malignant tumors within human breasts. Electromagnetic analysis is conducted using voxel datasets derived from human models to validate the approach. Tumor localization is achieved with high precision based on the Specific Absorption Rate (SAR) magnitude. The study considers various fat layer thicknesses (10–100 mm) and tumor radii (2.5–10 mm), addressing scattering effects comparable to the wavelength of the applied microwave radiation. The proposed Vivaldi antenna operates at 3.5 GHz, achieving a gain of 15.5 dBi with a half-power beamwidth in the E-plane of ±12°. Results demonstrate minimal average errors and high-performance indices (PI) for fat thickness (0.1 %, 90 %), tumor size (0.06 %, 94 %), and tumor classification (0.11 %, 89 %). The experimental and simulation results exhibit strong agreement, confirming the feasibility and potential of the proposed antenna system for medical diagnostics and post-detection rehabilitation planning.

Keywords: breast cancer; metasurface artificial neural network algorithms; microwave approach; rehabilitation; high gain Vivaldi antenna; tumor detection
Corresponding authors: Mohammad Alibakhshikenari, Electronics Engineering Department, University of Rome “Tor Vergata”, 00133 Rome, Italy, E-mail: ; and Taha A. Elwi, Islamic University Centre for Scientific Research, The Islamic University, Najaf, Iraq, E-mail:

Acknowledgments

The authors extend their appreciation to the King Salman center for Disability Research for funding this work through Research Group no KSRG-2024-117. Additionally, the authors would like to express their thanks to Dr. Saif Mohamed Baraa and Al-Bayan University, which offered the equipment and the opportunities that enabled this study by AlBayan University, Technical College of Engineering, Baghdad– 10011, Iraq.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: Authors declare that such methods were not used to write this paper.

  5. Conflict of interest: The authors declare no competing interests.

  6. Research funding: This work was funded by King Salman Center for Disability Research through Research Group no KSRG-2024-117.

  7. Data availability: All data generated or analyzed during this study are included in this article. Human data was not used in this study.

  8. Declaration: All of the figures, materials, and data within the manuscript are original and owned by authors.

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Received: 2024-06-12
Accepted: 2025-03-31
Published Online: 2025-04-25
Published in Print: 2025-08-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Microwave-based breast cancer detection using a high-gain Vivaldi antenna and metasurface neural network approach for medical diagnostics
  4. Design and implementation of on-body PEC backed 2 × 2 MIMO antenna
  5. Horn integrated 3-D printed four-port MIMO DRA for CubeSats
  6. On the performance investigation of a low profile UWB antenna backed with conjointly connected sickle shaped AMC structure for on-/off body communications
  7. Frequency and pattern reconfigurable patch antenna for multi-standard wireless applications
  8. A novel high isolation quad-port multiband MIMO antenna for V2X applications at Sub-6 GHz band
  9. Axial ratio control of circularly polarized microstrip antenna using miniaturized multilayer graphene resistive pads
  10. Subspace estimation of coherent wideband OFDM signals
  11. Dual-band SIW filter using slot perturbation
  12. Cuckoo search-ExtraTrees model for Radio-frequency power amplifier under different temperatures
https://doi.org/10.1515/freq-2024-0190
Keywords for this article
breast cancer; metasurface artificial neural network algorithms; microwave approach; rehabilitation; high gain Vivaldi antenna; tumor detection

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Microwave-based breast cancer detection using a high-gain Vivaldi antenna and metasurface neural network approach for medical diagnostics
  4. Design and implementation of on-body PEC backed 2 × 2 MIMO antenna
  5. Horn integrated 3-D printed four-port MIMO DRA for CubeSats
  6. On the performance investigation of a low profile UWB antenna backed with conjointly connected sickle shaped AMC structure for on-/off body communications
  7. Frequency and pattern reconfigurable patch antenna for multi-standard wireless applications
  8. A novel high isolation quad-port multiband MIMO antenna for V2X applications at Sub-6 GHz band
  9. Axial ratio control of circularly polarized microstrip antenna using miniaturized multilayer graphene resistive pads
  10. Subspace estimation of coherent wideband OFDM signals
  11. Dual-band SIW filter using slot perturbation
  12. Cuckoo search-ExtraTrees model for Radio-frequency power amplifier under different temperatures
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