Meta-cognitive q-Gaussian RBF network for binary classification: Application to mild cognitive impairment (MCI)

In this paper, we present a novel approach for classification of Mild Cognitive Impairment (MCI) and normal subjects from Magnetic Resonance Images (MRI) using a proposed `sequential Projection Based Learning for Meta-cognitive q-Gaussian Radial Basis Function Network (PBL-McqRBFN)´ classifier. The McqRBFN has two components, namely, a cognitive component and a meta-cognitive components. The cognitive component is a single hidden layer Radial Basis Function (RBF) network with a q-Gaussian activation function, that allows different RBF´s in one network, like the Gaussian, the Inverse Multiquadratic, and the Cauchy functions, by changing a real q-parameter. The meta-cognitive component present in McqRBFN helps in selecting proper samples to learn based on its current knowledge and evolve architecture automatically. The McqRBFN employs a sequential Projection Based Learning (PBL) algorithm to reduce the computational effort used in training. For simulation studies, we have used MRI data from the Alzheimer´s Disease Neuroimaging Initiative database. Voxel Based Morphometry (VBM) is used for feature extraction from MRI data and extracted VBM features are fed into the PBL-McqRBFN classifier. The experimental results show that our proposed PBL-McqRBFN classifier can accurately differentiate MCI and normal subjects.