Deep Learning Strategies for Quantitative Biomedical Microwave Imaging

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.