Abstract: “The goal of this contribution is to find out a set of Y slices (coronal slices) from MRIs of patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal images, that provides the maximum accuracy in a multiclass classification system. Images are preprocessed and 2D wavelet coefficients are extracted to form a feature matrix. Using a feature selection algorithm called mRMR, the best features from the matrix are extracted; then, the dimension of the feature vectors is reduced using PCA and finally, it is used to train an SVM to perform multi-class classification. In order to find the best combinations of coronal slices, a multi-objective genetic optimization methodology based on NSGA-II is used and a set of different solutions are extracted from the Pareto front. More relevant solutions are selected using more flexible criteria than that of the Pareto front, and examine what slices and accuracies are achieved. The multi-classification accuracies obtained by the pr”

Keywords: Alzheimer's disease, multiclass classification, feature selection, mild cognitive impairment, Multi-objective genetic algorithm optimization, coronal slices selection.