Etude de Modèles à base de réseaux Bayésiens pour l'aide au diagnostic de tumeurs cérébrales

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

The threshold EM algorithm for parameter learning in bayesian network with incomplete data

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. In order to limit the search space and escape from local maxima produced by executing EM algorithm, this paper presents a learning parameter algorithm that is a fusion of EM and RBE algorithms. This algorithm incorporates the range of a parameter into the EM algorithm. This range is calculated by the first step of RBE algorithm allowing a regularization of each parameter in bayesian network after the maximization step of the EM algorithm. The threshold EM algorithm is applied in brain tumor diagnosis and show some advantages and disadvantages over the EM algorithm.