| Autor: |
Mohammad Asefi, Ian Jeffrey, Joe LoVetri, Colin Gilmore, Vahab Khoshdel, Keeley Edwards |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS). |
| DOI: |
10.23919/ursigass51995.2021.9560264 |
| Popis: |
A recently developed neural network architecture for recovering the radius, height, and bulk complex-valued permittivity of the fibroglandular region of a human breast model from microwave measurements is extended to multiple frequencies. Results are presented for synthetic models with different sized fibroglandular regions both with and without a tumor present. The performance of this neural network architecture for single- and multi-frequency data in the 1.1 - 1.5 GHz range is demonstrated. Both neural networks are able to recover the desired bulk parameters of the fibroglandular region, with multi-frequency data leading to improved fibroglandular property estimates. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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