An automated supervised method for the diagnosis of Alzheimer’s disease based on fMRI data using weighted voting schemes

We present an automated supervised method which assists in the diagnosis of Alzheimerpsilas disease (AD) using fMRI data. The method consists of five stages: a) preprocessing of fMRI data to remove motion and spatial noise artifacts, b) modeling of the data using generalized linear models (GLM), c) feature extraction, d) feature selection and e) classification using majority and weighted voting schemes.