A Novel Convolutional Variation of Broad Learning System for Alzheimer’s Disease Diagnosis by Using MRI Images

Abstract

Alzheimer’s disease (AD) is a serious chronic health problem that causes great pain and loss to patients and their families. Its early and accurate diagnosis would achieve significant progress on the prevention and treatment of the disease. Magnetic Resonance Imaging (MRI) is a commonly used technique in nuclear medical diagnostics. However, it is still a challenging problem to diagnose AD, Control Normal (CN), and Mild Cognitive Impairment (MCI) because of the complex structures of MRI. In this paper, diagnosing models for MRI images are proposed to identify the various stages of AD based on the Broad Learning Systems (BLS), as well as its convolutional variants. To verify the validity of the proposed models, experiments on MRI images collected from the ADNI website are tested and evaluated. The results show that our algorithms outperform the other state-of-the-art algorithms for various tasks with better accuracy and less training times. Finally, the cross-domain learning ability of the proposed algorithms is verified on an independent AD dataset.