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.