Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of
  Clinical MRI Scans
Long Chen Liben Chen Binfeng Xu Wenxin Zhang Narges Razavian
The aging population of the U.S. drives the prevalence of Alzheimer's
disease. Brookmeyer et al. forecasts approximately 15 million Americans will
have either clinical AD or mild cognitive impairment by 2060. In response to
this urgent call, methods for early detection of Alzheimer's disease have been
developed for prevention and pre-treatment. Notably, literature on the
application of deep learning in the automatic detection of the disease has been
proliferating. This study builds upon previous literature and maintains a focus
on leveraging multi-modal information to enhance automatic detection. We aim to
predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive
Impairment (MCI), and Alzheimer's Disease (AD), based on two different types of
brain MRI scans. We design an AlexNet-based deep learning model that learns the
synergy of complementary information from both T1 and FLAIR MRI scans.