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

TL;DR

This paper explores Bayesian network models for diagnosing brain tumors, comparing structures derived from expert reasoning and automatic generation, and introduces an extended EM algorithm incorporating prior knowledge to improve parameter estimation.

Contribution

It presents a novel extension of the EM algorithm that integrates prior thresholds, enhancing Bayesian network parameter estimation from incomplete data for tumor diagnosis.

Findings

Bayesian networks effectively model diagnostic uncertainty.

Automatic structure generation improves diagnostic accuracy.

Extended EM algorithm with prior knowledge yields promising results.

Abstract

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