Research of CNN-SCN for Early Diagnosis of Alzheimer's Disease

Abstract

The early and effective diagnosis of Alzheimer's disease(AD) and mild cognitive impairment(MCI) has received increasing attention in recent years, but most of the existing studies do not pay enough attention to the spatial structure information in structural magnetic resonance images(MRI), and do not go deeper to explore the potential connection between spatial slices and slices, thus making the network models less accurate, poor generalization ability. To explore deeper the connection between spatial context slices, this research is designed to develop a new deep learning method to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability.