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Alzheimer’s Disease Detection Using Minimal Morphometric Features with an Extreme Learning Machine Classifier

  • M. Aswatha Kumar
  • B. S. Mahanand
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)

Abstract

In this paper, we present an accurate method of detection of Alzheimer’s disease using a minimal number of voxel-based morphometry features obtained from the brain MRI scans. The problem of early detection of AD is formulated as a binary classification problem and solved using an extreme learning machine classifier. The functional relationship between the voxel-based morphometry features extracted from magnetic resonance images and Alzheimer’s disease is approximated closely using the extreme learning machine classifier. Since, the extreme learning machine is computationally efficient and provides a better generalization ability, Principal Component Analysis along with the Extreme Learning Machine classifier (referred to here as the PCA-ELM classifier) is used to select the minimal set of morphometric features from the brain MRI images for Alzheimer’s disease detection. Performance of the PCA-ELM classifier is evaluated using the Open Access Series of Imaging Studies (OASIS) data set. The results are also compared with the well-known support vector machine classifier. The study results clearly show that the PCA-ELM classifier produces a better generalization performance with a minimal set of features.

Keywords

Support Vector Machine Extreme Learn Machine Hide Neuron Support Vector Machine Classifier Clinical Dementia Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringM.S. Ramaiah Institute of TechnologyBangaloreIndia
  2. 2.Department of Information Science and EngineeringSri Jayachamarajendra College of EngineeringMysoreIndia

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