Etude de Modèles à base de réseaux Bayésiens pour l'aide au diagnostic de tumeurs cérébrales
This work addresses the challenge of improving diagnostic accuracy for brain tumors, which is an incremental advancement in medical AI.
The authors tackled the problem of diagnosing brain tumors by developing Bayesian network models to handle uncertainty, and they achieved very encouraging results in diagnostic accuracy.
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