Stroke Classification Via Microwave Imaging Using Huygens' Principle Assisted by Deep Learning
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.
Authors
- Mirco Cosottini (ORCID: https://orcid.org/0000-0001-9400-6574)
- Moein Movafagh (ORCID: https://orcid.org/0000-0003-2159-7048)
- Gianluigi Tiberi (ORCID: https://orcid.org/0000-0001-8787-1295)
- Mohammad Ghavami (ORCID: https://orcid.org/0000-0001-8887-5052)
- Mehran Taghipour‐Gorjikolaie (ORCID: https://orcid.org/0000-0001-6132-8454)
- Sandra Dudley (ORCID: https://orcid.org/0000-0002-6431-5357)
- Navid Ghavami (ORCID: https://orcid.org/0000-0002-5102-0760)
Institutions
- University of Pisa (IT)
- London South Bank University (GB)
- United Biscuits (United Kingdom) (GB)
Publication Details
- Published
- 2025-03-30
- DOI
- https://doi.org/10.23919/eucap63536.2025.10999269
- Citations
- 1
- Primary Topic
- Photoacoustic and Ultrasonic Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.67