Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans
This addresses the need for early detection in the aging population, but it is incremental as it builds on existing literature with a focus on multi-modal fusion.
The study tackled the problem of early detection of Alzheimer's disease by predicting disease stages (Cognitively Normal, Mild Cognitive Impairment, Alzheimer's Disease) using multi-modal MRI scans, achieving unspecified results without concrete numbers.
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.