A Deep Pair Siamese CNN for Multi-Class Classification of Alzheimer Disease.

Alzheimer’s disease is a neurodegenerative disease characterized by a progressive loss memory and certain intellectual (cognitive) functions leading to repercussions in the activities of daily living. Early diagnosis of Alzheimer’s disease is a difficult task for researchers. In clinical research, magnetic resonance imaging (MRI) is used to diagnose Alzheimer’s disease. MRI can detect cortical atrophy and in particular atrophy of the hippocampi. Approaches based on deep convolutional neural network (CNN) and machine learning represent one solution and they are readily available and described to solve various problems related to the analysis of brain image data. High-dimensional classification approaches have been widely used to study magnetic resonance imaging (MRI) data for automatic classification of Alzheimer’s disease (AD). In this work, we proposed a Deep Siamese Convolutional Neural Network model for a Multi-class Classification of Dementia Stages in Alzheimer’s Disease. The experiments are carried out on the OASIS database accessible free of charge to the public. We compared our model with the best models and found that the proposed model outperforms the best models in terms of different performance.