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.

Artificial neural networks for quantitative microwave breast imaging

Ambrosanio M.;Franceschini S.;Baselice F.;Pascazio V.
2020-01-01

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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/86333
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