  In this paper, convergence results in a multivariate setting have been proved
for a family of neural network operators of the max-product type. In
particular, the coefficients expressed by Kantorovich type means allow to treat
the theory in the general frame of the Orlicz spaces, which includes as
particular case the $L^p$-spaces. Examples of sigmoidal activation functions
are discussed, for the above operators in different cases of Orlicz spaces.
Finally, concrete applications to real world cases have been presented in both
uni-variate and multivariate settings. In particular, the case of
reconstruction and enhancement of biomedical (vascular) image has been
discussed in details.
