MRI Segmentation of Brain Tissue and Course Classification in Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common diseases causing cognitive impairment in middle-aged and elderly people, high cost poses a challenge for health systems to cope with expected increasing number cases future. With advance aging society, China has largest patients world. Therefore, how diagnose early accurately intervene positively an urgent problem. In this paper, improved MultiRes + UNet network used effectively segment brain tissue preprocessing. This method expands convolutional field by null convolution integrate global information, mitigates differences between encoder–decoder features using block Res path structure, greatly reducing memory requirement, improving its accuracy, applicability, robustness. The non-local means attention model introduced make mapped organization categories free from noise interference. classification problem, paper adopts VoxCNN binary AD, EMCI, LMCI, NC. Experiments showed that performance accuracy rate significantly combined effect network, was 98.35% AD vs. NC, 89.46% 83.95% LMCI 88.27% EMCI