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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 5485080, 11 pages
https://doi.org/10.1155/2017/5485080
Research Article

Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

1National Research Center for Dementia, Gwangju, Republic of Korea
2Department of Software, Gachon University, 1342 Seongnamdaero, Sujeonggu, Seongnam, Gyeonggido 13120, Republic of Korea
3School of Electrical Engineering and Computer Science (EECS), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
4Department of Multimedia, Chonnam National University, 50 Daehakro, Yeosu, Jeollanamdo 59626, Republic of Korea

Correspondence should be addressed to Sang-Woong Lee; rk.ca.nohcag@eels

Received 4 December 2016; Revised 30 March 2017; Accepted 13 April 2017; Published 18 June 2017

Academic Editor: Ashish Khare

Copyright © 2017 Ramesh Kumar Lama et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.