Identification of brain regions responsible for Alzheimer’s disease using a Self-adaptive Resource Allocation Network

In this paper, we present a novel approach for the identification of brain regions responsible for Alzheimer’s disease using the Magnetic Resonance (MR) images. The approach incorporates the recently developed Self-adaptive Resource Allocation Network (SRAN) for Alzheimer’s disease classification us...

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Veröffentlicht in:Neural networks Jg. 32; S. 313 - 322
Hauptverfasser: Mahanand, B.S., Suresh, S., Sundararajan, N., Aswatha Kumar, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.08.2012
Schlagworte:
Aged
Algorithms
Alzheimer Disease - pathology
Alzheimer’s disease
Amygdala - pathology
Artificial Intelligence
Brain - pathology
Humans
Image Processing, Computer-Assisted
Integer coded genetic algorithm
Magnetic Resonance Imaging
Models, Genetic
Neural Networks (Computer)
Neurons - classification
Parahippocampal Gyrus - pathology
Resource Allocation - statistics & numerical data
Self-adaptive Resource Allocation Network
Support Vector Machine
Voxel-based morphometry
Magnetic Resonance Imaging
Integer coded genetic algorithm
Alzheimer’s disease
Voxel-based morphometry
Self-adaptive Resource Allocation Network
ISSN:0893-6080, 1879-2782, 1879-2782
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:In this paper, we present a novel approach for the identification of brain regions responsible for Alzheimer’s disease using the Magnetic Resonance (MR) images. The approach incorporates the recently developed Self-adaptive Resource Allocation Network (SRAN) for Alzheimer’s disease classification using voxel-based morphometric features of MR images. SRAN classifier uses a sequential learning algorithm, employing self-adaptive thresholds to select the appropriate training samples and discard redundant samples to prevent over-training. These selected training samples are then used to evolve the network architecture efficiently. Since, the number of features extracted from the MR images is large, a feature selection scheme (to reduce the number of features needed) using an Integer-Coded Genetic Algorithm (ICGA) in conjunction with the SRAN classifier (referred to here as the ICGA–SRAN classifier) have been developed. In this study, different healthy/Alzheimer’s disease patient’s MR images from the Open Access Series of Imaging Studies data set have been used for the performance evaluation of the proposed ICGA–SRAN classifier. We have also compared the results of the ICGA–SRAN classifier with the well-known Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. The study results clearly show that the ICGA–SRAN classifier produces a better generalization performance with a smaller number of features, lower misclassification rate and a compact network. The ICGA–SRAN selected features clearly indicate that the variations in the gray matter volume in the parahippocampal gyrus and amygdala brain regions may be good indicators of the onset of Alzheimer’s disease in normal persons.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2012.02.035