Aided Diagnosis of Alzheimer's Disease Based on Structural Magnetic Resonance Imaging

With the vigorous development of computer and pattern recognition technologies, using magnetic resonance imaging (MRI) and machine learning methods to assist diagnosis of Alzheimer's disease (AD) has became a research hotspot. A new method based on Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Linear Discriminant Analysis (LDA) for aided diagnosis of AD is proposed in this study. Firstly, Structural MRI images including 34 patients with AD, 26 subjects with subjective memory complaints (SMC) and 50 normal controls (NC) from the ADNI database are preprocessed to obtain the gray matter volumes of 90 brain regions. Then the fusion idea on SVM-RFE and LDA is used to select the characteristics on the above gray matter volumes. Finally, the selected features are classified by support vector machine (SVM), and the average classification accuracies of AD/NC, AD/SMC and NC/SMC reach respectively 94%, 100% and 93.6%. Compared with SVM-RFE or LDA alone, the average classification results on the fusion idea have obvious advantages. The above experimental results show that the proposed method can effectively extract features and assist doctors for exact diagnosis of AD and SMC.