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Authors: M. Ambrosanio ; S. Franceschini ; F. Baselice and V. Pascazio

Affiliation: Department of Engineering, University of Naples Parthenope, Naples, Italy

Keyword(s): Microwave Imaging, Inverse Scattering, Artificial Neural Network, MIMO Systems, Biomedical Imaging.

Abstract: This paper is focused on the use of artificial neural networks (ANNs) for biomedical microwave imaging of breast tissues in the framework of advanced breast cancer imaging techniques. The proposed scheme processes the scattered field collected at receivers locations of a multiview-multistatic system and aims at providing an estimate of the morphological and dielectric features of the breast tissues, which represents a strongly nonlinear scenario with several challenging aspects. In order to train the network, a simulated data set has been created by implementing the forward problem and an automatic randomly-shaped breast profile generator based on the statistical distribution of complex permittivity of breast biological tissues was developed. Some numerical tests were carried out to evaluate the performance of the proposed method and, in conclusion, we found that the use of ANNs for quantitative biomedical imaging purposes seems to be very promising.

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Paper citation in several formats:
Ambrosanio, M., Franceschini, S., Baselice, F. and Pascazio, V. (2020). Artificial Neural Networks for Quantitative Microwave Breast Imaging. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOIMAGING; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 204-208. DOI: 10.5220/0009172802040208

@conference{bioimaging20,
author={M. Ambrosanio and S. Franceschini and F. Baselice and V. Pascazio},
title={Artificial Neural Networks for Quantitative Microwave Breast Imaging},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOIMAGING},
year={2020},
pages={204-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009172802040208},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOIMAGING
TI - Artificial Neural Networks for Quantitative Microwave Breast Imaging
SN - 978-989-758-398-8
IS - 2184-4305
AU - Ambrosanio, M.
AU - Franceschini, S.
AU - Baselice, F.
AU - Pascazio, V.
PY - 2020
SP - 204
EP - 208
DO - 10.5220/0009172802040208
PB - SciTePress

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