Alzheimer's disease detection using a projection based learning meta-cognitive RBF network

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 Imagin...

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Main Authors: Sateesh Babu, Giduthuri, Suresh, Sundaram, Mahanand, Belathur Suresh
Other Authors: School of Computer Engineering
Format: Conference Paper
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/97861
http://hdl.handle.net/10220/12405
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author Sateesh Babu, Giduthuri
Suresh, Sundaram
Mahanand, Belathur Suresh
author2 School of Computer Engineering
author_facet School of Computer Engineering
Sateesh Babu, Giduthuri
Suresh, Sundaram
Mahanand, Belathur Suresh
author_sort Sateesh Babu, Giduthuri
collection NTU
description 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.
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spelling ntu-10356/978612020-05-28T07:17:16Z Alzheimer's disease detection using a projection based learning meta-cognitive RBF network Sateesh Babu, Giduthuri Suresh, Sundaram Mahanand, Belathur Suresh School of Computer Engineering International Joint Conference on Neural Networks (2012 : Brisbane, Australia) DRNTU::Engineering::Computer science and engineering 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. 2013-07-26T07:03:27Z 2019-12-06T19:47:26Z 2013-07-26T07:03:27Z 2019-12-06T19:47:26Z 2012 2012 Conference Paper 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). https://hdl.handle.net/10356/97861 http://hdl.handle.net/10220/12405 10.1109/IJCNN.2012.6252419 en © 2012 IEEE.
spellingShingle DRNTU::Engineering::Computer science and engineering
Sateesh Babu, Giduthuri
Suresh, Sundaram
Mahanand, Belathur Suresh
Alzheimer's disease detection using a projection based learning meta-cognitive RBF network
title Alzheimer's disease detection using a projection based learning meta-cognitive RBF network
title_full Alzheimer's disease detection using a projection based learning meta-cognitive RBF network
title_fullStr Alzheimer's disease detection using a projection based learning meta-cognitive RBF network
title_full_unstemmed Alzheimer's disease detection using a projection based learning meta-cognitive RBF network
title_short Alzheimer's disease detection using a projection based learning meta-cognitive RBF network
title_sort alzheimer s disease detection using a projection based learning meta cognitive rbf network
topic DRNTU::Engineering::Computer science and engineering
url https://hdl.handle.net/10356/97861
http://hdl.handle.net/10220/12405
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