An enhanced neural system for biomedical image classification

Comparison and classification of images obtained from a single or more patients, at different times but with the same procedure, is important in evaluating the origin or the degree of several pathologies. As well, image classification fusing data acquired from different sources is often needed to locate regions or volumes, to analyse complex scenes or to simulate a diagnosis prediction. In this paper we present an enhanced neural system able to locate and classify tissue densitometric alterations in CT/MR image sequences; such a system has been optimised in order to reduce the computational complexity and the computational time.