  This article describes different models based on Bayesian networks RB
modeling expertise in the diagnosis of brain tumors. Indeed, they are well
adapted to the representation of the uncertainty in the process of diagnosis of
these tumors. In our work, we first tested several structures derived from the
Bayesian network reasoning performed by doctors on the one hand and structures
generated automatically on the other. This step aims to find the best structure
that increases diagnostic accuracy. The machine learning algorithms relate
MWST-EM algorithms, SEM and SEM + T. To estimate the parameters of the Bayesian
network from a database incomplete, we have proposed an extension of the EM
algorithm by adding a priori knowledge in the form of the thresholds calculated
by the first phase of the algorithm RBE . The very encouraging results obtained
are discussed at the end of the paper
