Supervised Classification Techniques for Identifying Alzheimer’s Disease

Abstract

Alzheimer’s Disease is a serious form of dementia. With no current cure, treatments focus on slowing the progression of the disease and controlling its symptoms. Early diagnosis by studying the biomarkers found in structural MRI is the key. This paper proposes a method which combines texture features extracted from gray level co-occurrence matrix and voxel-based morphometry neuroimaging analysis to classify Alzheimer’s disease patients. Different supervised classification techniques are studied, support vector machine, k-nearest neighbor, and decision tree, to obtain best identification accuracy. The paper explores as well the discriminative power for Alzheimer’s disease of certain anatomical regions of interest. The proposed technique is applied on gray matter tissues, and managed successfully to differentiate between Alzheimer’s disease patients and normal controls with accuracy 92%.