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      Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network

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      Author
      Mahanand, Belathur Suresh
      Suresh, Sundaram
      Sundararajan, Narasimhan
      Kumar, M. Aswatha
      Date of Issue
      2012
      School
      School of Computer Engineering
      Abstract
      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.
      Subject
      DRNTU::Engineering::Computer science and engineering
      Type
      Journal Article
      Series/Journal Title
      Neural networks
      Rights
      © 2012 Elsevier Ltd.
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      • SCSE Journal Articles
      http://dx.doi.org/10.1016/j.neunet.2012.02.035
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