  Conventional breast cancer imaging techniques are nowadays based on the use
of ionising radiations or ultrasound waves for the inspection of breast areas.
Nevertheless, these conventional techniques present some drawbacks related to
patient safety, processing time and resolution issues. In this framework,
microwave imaging can represent a valid alternative or a complementary
technique compared to other conventional medical imaging modalities since it is
safe (using non-ionising radiations), relatively cheap and more comfortable
from patient point of view. Unfortunately, it is slow and computationally
expensive, which strongly limit its use in clinical scenarios. In this paper,
an artificial neural network for effective and almost real-time breast imaging
is proposed. First, a realistic breast-like phantom generator was developed for
training the network. Subsequently, numerical analyses have been conducted for
the optimisation and the performance evaluation of the approach. The results
seem very promising in terms of recovery performance as well as for the
computation burden.
