Stroke Classification via Microwave Imaging using Huygens’ Principle Assisted by Deep Learning

Mohammad Ghavami; Gianluigi Tiberi; Sandra Dudley-Mcevoy; Moein Movafagh; Navid Ghavami; Mehran Taghipour Gorjikolaie

doi:10.23919/EuCAP63536.2025.10999269

Stroke Classification via Microwave Imaging using Huygens’ Principle Assisted by Deep Learning

Mohammad Ghavami, Gianluigi Tiberi, Sandra Dudley-Mcevoy, Moein Movafagh, Navid Ghavami, Mehran Taghipour Gorjikolaie

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

11 Downloads (Pure)

Abstract

The use of microwave imaging techniques for detection and classification of brain strokes is a growing field of research, motivated by the distinct dielectric properties of hemorrhagic and ischemic strokes relative to their surrounding tissues. In this work we combine our image processing algorithm based on the Huygens’ principle (HP) and U-net based deep learning to produce morphological reconstructed images, allowing detection and classification of brain strokes.

Original language	English
Number of pages	3
DOIs	https://doi.org/10.23919/EuCAP63536.2025.10999269
Publication status	Published - 4 Apr 2025
Event	European Conference on Antennas and Propagation - Stockholm, Sweden, Stockholm, Sweden Duration: 30 Mar 2025 → 4 Apr 2025 https://www.eucap2025.org/

Conference

Conference	European Conference on Antennas and Propagation
Abbreviated title	EuCAP 2025
Country/Territory	Sweden
City	Stockholm
Period	30/03/25 → 4/04/25
Internet address	https://www.eucap2025.org/

Keywords

Deep learning
Huygens’ principle
Microwave imaging

Access to Document

10.23919/EuCAP63536.2025.10999269

1571086630_finalAccepted author manuscript, 427 KBLicence: CC BY 4.0

Cite this

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title = "Stroke Classification via Microwave Imaging using Huygens{\textquoteright} Principle Assisted by Deep Learning",

abstract = "The use of microwave imaging techniques for detection and classification of brain strokes is a growing field of research, motivated by the distinct dielectric properties of hemorrhagic and ischemic strokes relative to their surrounding tissues. In this work we combine our image processing algorithm based on the Huygens{\textquoteright} principle (HP) and U-net based deep learning to produce morphological reconstructed images, allowing detection and classification of brain strokes.",

keywords = "Deep learning, Huygens{\textquoteright} principle, Microwave imaging",

author = "Mohammad Ghavami and Gianluigi Tiberi and Sandra Dudley-Mcevoy and Moein Movafagh and Navid Ghavami and \{Taghipour Gorjikolaie\}, Mehran",

year = "2025",

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doi = "10.23919/EuCAP63536.2025.10999269",

language = "English",

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T1 - Stroke Classification via Microwave Imaging using Huygens’ Principle Assisted by Deep Learning

AU - Ghavami, Mohammad

AU - Tiberi, Gianluigi

AU - Dudley-Mcevoy, Sandra

AU - Movafagh, Moein

AU - Ghavami, Navid

AU - Taghipour Gorjikolaie, Mehran

PY - 2025/4/4

Y1 - 2025/4/4

N2 - The use of microwave imaging techniques for detection and classification of brain strokes is a growing field of research, motivated by the distinct dielectric properties of hemorrhagic and ischemic strokes relative to their surrounding tissues. In this work we combine our image processing algorithm based on the Huygens’ principle (HP) and U-net based deep learning to produce morphological reconstructed images, allowing detection and classification of brain strokes.

AB - The use of microwave imaging techniques for detection and classification of brain strokes is a growing field of research, motivated by the distinct dielectric properties of hemorrhagic and ischemic strokes relative to their surrounding tissues. In this work we combine our image processing algorithm based on the Huygens’ principle (HP) and U-net based deep learning to produce morphological reconstructed images, allowing detection and classification of brain strokes.

KW - Deep learning

KW - Huygens’ principle

KW - Microwave imaging

U2 - 10.23919/EuCAP63536.2025.10999269

DO - 10.23919/EuCAP63536.2025.10999269

M3 - Paper

T2 - European Conference on Antennas and Propagation

Y2 - 30 March 2025 through 4 April 2025

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