Abstract
This article proposes a U-Net convolution neural network (U-net CNN) with segmentation capability, to classify the types of human tissue in the biomedical inverse scattering scenario. The inverse scattering imaging algorithm, subspace-based optimization method (SOM) is firstly used to obtain distribution of dielectric permittivity of human tissues. The obtained result is input into the pre-trained U-Net CNN, to output the precise segmentation masks (classification images). Numerical results using synthetic data is used to validate the feasibility of precise classification of human tissues imaging.
Original language | English |
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Title of host publication | 2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350383317 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 - Nanjing, China Duration: 15 Apr 2024 → 17 Apr 2024 |
Publication series
Name | 2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 - Proceedings |
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Conference
Conference | 2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 |
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Country/Territory | China |
City | Nanjing |
Period | 15/04/24 → 17/04/24 |
Keywords
- SOM
- U-Net CNN
- biomedical imaging
- inverse scattering imaging
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He, Z., Du, N., Wang, J., & Ye, X. (2024). A Machine Learning Classification Approach for Solving Biomedical Inverse Scattering Problem. In 2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 - Proceedings (2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCEM60619.2024.10558921