This paper proposes a numerical performance as-sessment of the recovery capabilities in the framework of mi-crowave imaging via deep learning approaches. More in detail, aim of the analysis is the comparison among different convo-lutional neural network architectures in order to understand the impact of each parameter on the recovery performance for quantitative imaging. To support the analysis, some quality metrics were evaluated and a comparison with a conventional nonlinear approach is considered. The results seem promising, both in terms of computational time and recovery accuracy, especially in very noisy scenarios with a limited amount of data.

Deep Learning Strategies for Quantitative Biomedical Microwave Imaging

Autorino M. M.
;
Franceschini S.;Ambrosanio M.;Baselice F.;Pascazio V.
2022

Abstract

This paper proposes a numerical performance as-sessment of the recovery capabilities in the framework of mi-crowave imaging via deep learning approaches. More in detail, aim of the analysis is the comparison among different convo-lutional neural network architectures in order to understand the impact of each parameter on the recovery performance for quantitative imaging. To support the analysis, some quality metrics were evaluated and a comparison with a conventional nonlinear approach is considered. The results seem promising, both in terms of computational time and recovery accuracy, especially in very noisy scenarios with a limited amount of data.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/110837
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