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This article describes different models based on Bayesian networks RBmodeling expertise in the diagnosis of brain tumors. Indeed, they are welladapted to the representation of the uncertainty in the process of diagnosis ofthese tumors. In our work, we first tested several structures derived from theBayesian network reasoning performed by doctors on the one hand and structuresgenerated automatically on the other. This step aims to find the best structurethat increases diagnostic accuracy. The machine learning algorithms relateMWST-EM algorithms, SEM and SEM + T. To estimate the parameters of the Bayesiannetwork from a database incomplete, we have proposed an extension of the EMalgorithm by adding a priori knowledge in the form of the thresholds calculatedby the first phase of the algorithm RBE . The very encouraging results obtainedare discussed at the end of the paper

Etude de Mod\`eles \`a base de r\'eseaux Bay\'esiens pour l'aide au diagnostic de tumeurs c\'er\'ebrales