Feature selection using kernel PCA for Alzheimer's disease detection with 3D MR Images of brain

This paper investigates the application of the kernel PCA to select the features that produced by an extraction feature method, i.e. complete local binary pattern from three orthogonal planes. The proposed approach is used to detect Alzheimer's disease using 3D Magnetic Resonance Images (MRI) of brain. In this study, the feature extraction method is done by using the different radius and the number of different neighbors. A support vector machine classifier is adapted to discriminant normal from Alzheimer's, normal from mild cognitive impairment (MCI) and MCI from Alzheimer's. The experimental results show our proposed method achieves an accuracy of 100% for classification of Alzheimer's and normal. This accuracy result is also achieved by MCI and normal classification, whereas the accuracy of Alzheimer's and MCI classification is only 84%.