Ninth International Conference on Advances in Bio-Informatics, Bio-Technology and Environmental Engineering - ABBE 2019
Author(s) : ANTONIO CARRILLO , IGNACIO ROJAS , OLGA VALENZUELA
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 proposed method are 94.2% (the individual of the Pareto front with the highest accuracy) or 91.9% (using the best 13 slices according to their frequency of presence in the Pareto front). It is important to note that not only a good accuracy is obtained in the classification, but also new knowledge about the most relevant coronal slices to distinguish the four MRI classes (AD, Normal, MCI, LMCI).. Keywords- Alzheimerâ€™s Disease (AD), Magnetic Resonance Image (MRI), Discrete Wavelet Transform (DWT), minimum Redundancy Maximum Relevance (mRMR), Support Vector Machine (SVM), Genetic Algorithms (GA).