A Bayesian model for brain tumor classification using clinical-based features

This paper tackles the problem of automatic brain tumor classification from Magnetic Resonance Imaging (MRI) where, traditionally, general-purpose texture and shape features extracted from the Region of Interest (tumor) have become the usual parameterization of the problem. Two main contributions are made in this context. First, a novel set of clinical-based features that intend to model intuitions and expert knowledge of physicians is suggested. Second, a system is proposed that is able to fuse multiple individual scores (based on a particular MRI sequence and a pathological indicator present in that sequence) by using a Bayesian model that produces a global system decision. This approximation provides a quite flexible solution able to handle missing data, which becomes a very likely case in a realistic scenario where the number clinical tests varies from one patient to another. Furthermore, the Bayesian model provides extra information concerning the uncertainty of the final decision. Our experimental results prove that the use of clinical-based feature leads to a significant increment of performance in terms of Area Under the Curve (AUC) when compared to a state-of-the art reference. Furthermore, the proposed Bayesian fusion model clearly outperforms other fusion schemes, especially when few diagnostic tests are available.