fMRI classification of Alzheimer's disease in the Brain Network using GA-PSO

Alzheimer's disease (AD) is a progressive degenerative brain disease and one of the most common dementias in the elderly population. In recent years, deep learning and artificial intelligence techniques have been widely used in the diagnosis and research of AD. The purpose of this study is to develop an improved hybrid model based on a genetic algorithm (GA) and a particle swarm algorithm (PSO) to find the optimal feature combination for Alzheimer's disease classification and detection. First, the phase synchronization index (PSI) method is used to extract geometric features from the Alzheimer's disease ROI(Regions of Interests) signals extracted from the AAL(Automated Anatomical Labeling )-116 atlas; second, the genetic algorithm (GA) with Gaussian variation (GDM) and the particle swarm algorithm (PSO) are combined with the improved optimization algorithm for feature selection, the GA-PSO; finally, the combination of features is fed to an SVM classifier for detection. The method proposed in this paper uses a 5-fold cross-validation strategy in feature combination and achieves a classification accuracy of 84.92%, which is an effective AD detection method that can help clinicians to quickly diagnose Alzheimer's disease based on PSI brain network topology analysis and feature selection optimization algorithm.