Meta-cognitive learning neural classifier for Alzheimer’s disease detection

Abstract

In this paper, we present an approach for Alzheimer’s Disease (AD) detection from Magnetic Resonance Images (MRI) using Meta-cognitive Radial Basis Function Network (McRBFN) classifier. The McRBFN classifier uses Voxel Based Morphometric (VBM) features extracted from MRI and employs a sequential Projection Based Learning (PBL) algorithm for classification. The meta-cognitive learning present in PBL-McRBFN helps in selecting proper samples to learn based on its current knowledge and evolve the architecture automatically. The study has been conducted using the well-known Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We compared the performance of the proposed classifier with reported results of existing classifiers in the literature. The performance results clearly indicates the better performance of PBL-McRBFN classifier for AD detection