Fasu-Net: Fast Alzheimer’s Disease Screening with Undersampled MRI Using Convolutional Neural Networks

Abstract

Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative condition, which results in dementia. Mild Cognitive Impairment (MCI) is an intermediate state between normal aging and AD. Instead of traditional questionnaire method, magnetic resonance imaging (MRI) can be used by radiologists to diagnose and screening AD recently, but long acquisition time is not conducive to screening AD and MCI. To solve this problem, we develop a Fasu-Net (Fast Alzheimer’s disease Screening neural network with Undersampled MRI) for AD and MCI clinical classification. The network uses undersampled structural MRI with a shorter acquisition time to improve the screening and diagnosis efficiency of AD. For achieving the best classification result, three axial planes of brain MR images were feed into the Fasu-Net with transfer learning method. The experiment results on undersampled 3D T1-weighted images database (ADNI) show that in the AD versus MCI versus HC (Healthy Controls) classification, the Fasu-Net achieved the accuracy of 91.41%, thus can be a potential method for fast clinical screening of AD.