Evaluation of Alzheimer’s disease by analysis of MR images using multilayer perceptrons and committee machines

Abstract Alzheimer’s disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and its correlation with the advance of Alzheimer’s disease. The MR images were acquired from a unique volunteer with Alzheimer’s, using an image system based on a clinical 1.5 T tomographer. The classification methods are based on multilayer perceptrons and committee machines and the classification results are used to correlate clinical and imaging findings. The classification results are used to improve the usual analysis of the ADC map.