@inproceedings{653b027f194940e8afbe6a8b48f4a326,
title = "A Machine Learning Classification Approach for Solving Biomedical Inverse Scattering Problem",
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.",
keywords = "SOM, U-Net CNN, biomedical imaging, inverse scattering imaging",
author = "Zi He and Naike Du and Jing Wang and Xiuzhu Ye",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 ; Conference date: 15-04-2024 Through 17-04-2024",
year = "2024",
doi = "10.1109/ICCEM60619.2024.10558921",
language = "English",
series = "2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024 - Proceedings",
address = "United States",
}