Fully Connected Tensor Network based Brain Structural Feature Extraction for Early Alzheimer's Disease Detection

Abstract

Alzheimer’s disease (AD) is an incurable neurodegenerative disease that involves structural changes in the brain. Early diagnosis of AD helps provide timely treatment and delay its progressive process. Many studies have been conducted based on brain images to detect AD. However, these works are mostly designed based on multi-modal brain data. The existing methods on uni-modal data, e.g., diffusion magnetic resonance imaging (dMRI) data, are not very effective or inexplainable. In this work, we propose the first tensor-based method for detecting early-stage AD. First, the brain data are segmented into blocks and concatenated as high-order tensors. The structural information in each block is preserved. Second, the fully connected tensor network (FCTN) is adopted. FCTN has strong expression capability for high-order data. This is the first work that exploits the advantage of FCTN for structural feature extraction. In the classification experiments, our method surpasses the existing methods 5% to 15% in accuracy.