Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Threat Assessment of Buried Objects Using Single-Frequency Microwave Measurements.

Sensors (Basel, Switzerland)·2025
Same author

A direct learning approach for detection of hotspots in microwave hyperthermia treatments.

Medical & biological engineering & computing·2025
Same author

XGBoost Enhances the Performance of SAFE: A Novel Microwave Imaging System for Early Detection of Malignant Breast Cancer.

Cancers·2025
Same author

Comparison of Microwave Hyperthermia Applicator Designs with Fora Dipole and Connected Array.

Sensors (Basel, Switzerland)·2023
Same author

Gradient-Boosting Algorithm for Microwave Breast Lesion Classification-SAFE Clinical Investigation.

Diagnostics (Basel, Switzerland)·2022
Same author

Rotationally Adjustable Hyperthermia Applicators: A Computational Comparative Study of Circular and Linear Array Applicators.

Diagnostics (Basel, Switzerland)·2022
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

522

Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia.

Gulsah Yildiz1, Halimcan Yasar1, Ibrahim Enes Uslu1

  • 1Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul 34469, Turkey.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary

This study introduces a deep-learning approach for microwave hyperthermia (MH) antenna calibration. The convolutional neural network (CNN) method optimizes antenna excitations more effectively than traditional look-up tables, improving treatment precision.

Keywords:
antenna excitation optimizationbreast cancerdeep learningenergy focushyperthermia treatment planningmicrowave hyperthermia

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
In Vitro and In Vivo Delivery of Magnetic Nanoparticle Hyperthermia Using a Custom-Built Delivery System
06:45

In Vitro and In Vivo Delivery of Magnetic Nanoparticle Hyperthermia Using a Custom-Built Delivery System

Published on: July 2, 2020

4.4K

Related Experiment Videos

Last Updated: Aug 29, 2025

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

522
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.6K
In Vitro and In Vivo Delivery of Magnetic Nanoparticle Hyperthermia Using a Custom-Built Delivery System
06:45

In Vitro and In Vivo Delivery of Magnetic Nanoparticle Hyperthermia Using a Custom-Built Delivery System

Published on: July 2, 2020

4.4K

Area of Science:

  • Biomedical Engineering
  • Medical Physics
  • Computational Electromagnetics

Background:

  • Microwave hyperthermia (MH) requires precise antenna calibration for targeted energy delivery.
  • Current methods often simplify the electric field and are system-specific, limiting broad application.
  • Existing optimization techniques may not fully capture the complex vector nature of electromagnetic fields.

Purpose of the Study:

  • To develop a versatile antenna excitation optimization scheme for microwave hyperthermia.
  • To implement and evaluate a convolutional neural network (CNN)-based approach for MH antenna calibration.
  • To demonstrate the superiority of the proposed method over conventional techniques.

Main Methods:

  • A novel antenna excitation optimization scheme was proposed, applicable to various MH configurations.
  • A convolutional neural network (CNN) was trained using superposed data from individual antenna elements.
  • The CNN-based approach was tested on two distinct MH applicator configurations.

Main Results:

  • CNN models demonstrated superior performance compared to look-up table methods.
  • Phase-only optimization achieved a 27% lower hotspot-to-target energy ratio.
  • Phase-power-combined optimization resulted in a 4% lower hotspot-to-target energy ratio.

Conclusions:

  • The proposed deep-learning-based optimization technique offers a universal protocol for MH antenna excitation.
  • This method enhances treatment precision by minimizing energy deposition in non-target tissues.
  • The approach facilitates effective calibration and comparison across different MH applicators.