Alzheimer’s Disease Detection Using Minimal Morphometric Features with an Extreme Learning Machine Classifier
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.
KeywordsSupport Vector Machine Extreme Learn Machine Hide Neuron Support Vector Machine Classifier Clinical Dementia Rate
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