Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97861
Title: Alzheimer's disease detection using a projection based learning meta-cognitive RBF network
Authors: Sateesh Babu, Giduthuri
Suresh, Sundaram
Mahanand, Belathur Suresh
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Sateesh Babu, G., Suresh, S., & Mahanand, B. S. (2012). Alzheimer's disease detection using a projection based learning meta-cognitive RBF network. The 2012 International Joint Conference on Neural Networks (IJCNN).
Abstract: In this paper, we present a novel approach with Voxel-Based Morphometry (VBM) detected features using a proposed `Projection Based Learning for Meta-cognitive Radial Basis Function Network (PBL-McRBFN)' classifier for the detection of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans. McRBFN emulates human-like meta-cognitive learning principles. As each sample is presented to the network, McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, its parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. Moreover, as samples with similar information are deleted, over-training is avoided. The PBL algorithm helps to reduce the computational effort used in training. For simulation studies, we have used well-known open access series of imaging studies data set. The performance of the PBL-McRBFN classifier is evaluated on complete morphometric features set obtained from the VBM analysis and also on reduced features sets from Independent Component Analysis (ICA). The performance evaluation study clearly indicates the superior performance of PBL-McRBFN classifier over results reported in the literature.
URI: https://hdl.handle.net/10356/97861
http://hdl.handle.net/10220/12405
DOI: http://dx.doi.org/10.1109/IJCNN.2012.6252419
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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