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Support Vector Machine Based Detection of Alzheimer’s Disease Using Brain Pattern Analysis

D. Selvathi, T. Emala


Alzheimer’s disease (AD) is the most common type of dementia in worldwide. Magnetic Resonance Imaging (MRI) is an important diagnostic tool that provides high resolution images and a high brain tissue contrast. In this paper, an approach for classification of brain Magnetic Resonance (MR) images into healthy and Alzheimer’s disease brain image are presented. The approach builds up a saliency map, which extract features from intensity, orientation and edges. This is accomplished by a fusion strategy that mixes mutually bottom up and top down information. Extracted features are given as the input of image classification using support vector machines. A set of 198 brain MR images from pathological (100) and healthy (98) subjects extracted from the Open Access Series of Imaging Studies (OASIS) database. The classification accuracy obtained using this SVM method is 70%.

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