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Updated: Mar 27, 2026

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Detecting breast cancer using microwave imaging and stochastic optimization.

Aleksandar Jeremic, Elham Khoshrowshahli

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 7, 2016

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This study explores stochastic optimization for early breast cancer detection using microwave imaging. The research aims to improve detection rates by modeling cancer as a high-conductivity scatterer, offering a safer alternative to mammography.

Area of Science:

Biomedical Engineering
Medical Imaging
Computational Electromagnetics

Background:

Early breast cancer detection is crucial for curability.
Mammography, while common, has limitations.
Microwave imaging presents a potentially cheaper and safer alternative for breast cancer diagnostics.

Purpose of the Study:

To evaluate the applicability of stochastic optimization techniques for breast cancer detection using microwave imaging.
To model breast cancer as a high-conductivity scatterer for improved detection accuracy.
To assess the impact of noise levels on detection rates in numerical models.

Main Methods:

Utilized stochastic optimization techniques.
Employed numerical models to simulate breast cancer detection scenarios.

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Last Updated: Mar 27, 2026

Clinical Imaging of Microwave Mammography

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Published on: November 14, 2025

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

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

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Analyzed the relationship between noise levels and detection rates.

Built upon a previous maximum likelihood-based detection method.

Main Results:

Stochastic optimization shows applicability for breast cancer detection via microwave imaging.
Numerical models provide insights into required noise levels for effective detection.
The conductivity contrast between cancerous and healthy tissue is a key physical property utilized.

Conclusions:

Stochastic optimization is a viable technique for enhancing breast cancer detection with microwave imaging.
Understanding noise thresholds is critical for achieving desired detection rates in microwave-based systems.
Further research with numerical models can guide the development of more sensitive and accurate breast cancer diagnostic tools.