Detection of onset of Alzheimer's disease from MRI images using a GA-ELM-PSO classifier

In this paper, a novel method for detecting the onset of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans is presented. It uses a combination of three different machine learning algorithms in order to get improved results and is based on a three-class classification problem. The three classes for classification considered in this study are normal, very mild AD and mild and moderate AD subjects. The machine learning algorithms used are: the Extreme Learning Machine (ELM) for classification, with its performance optimized by a Particle Swarm Optimization (PSO) and a Genetic algorithm (GA) used for feature selection. A Voxel-Based Morphometry (VBM) approach is used for feature extraction from the MRI images and GA is used to reduce the high dimensional features needed for classification. The GA-ELM-PSO classifier yields an average training accuracy of 94.57 % and a testing accuracy of 87.23 %, averaged across the three classes, over ten random trials. The results clearly indicate that the proposed approach can differentiate between very mild AD and normal cases more accurately, indicating its usefulness in detecting the onset of AD.