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.
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Aswatha Kumar, M., Mahanand, B.S. (2013). Alzheimer’s Disease Detection Using Minimal Morphometric Features with an Extreme Learning Machine Classifier. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_90
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DOI: https://doi.org/10.1007/978-81-322-0740-5_90
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0739-9
Online ISBN: 978-81-322-0740-5
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