Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

Author(s)

Folego, Guilherme ^{[1, 2]} ; Weiler, Marina ^[3] ; Casseb, Raphael F. ^[4] ; Pires, Ramon ^[1] ; Rocha, Anderson ^[1] ; I, Alzheimer's Dis Neuroimaging ; Lif, Australian Imaging Biomarkers

Affiliation

^[1] Univ Estadual Campinas, Inst Comp, Campinas - Brazil

^[2] CPQD, Campinas - Brazil

^[3] NIA, Lab Behav Neurosci, NIH, Intramural Res Program, Baltimore, MD 21224 - USA

^[4] Univ Calgary, Cumming Sch Med, Seaman Family MR Res Ctr, Calgary, AB - Canada

• Document type: Journal article
• Source: FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY; v. 8, OCT 30 2020.
• Web of Science Citations: 0

Abstract

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases. (AU)

• FAPESP's process: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events
• Grantee: Anderson de Rezende Rocha
• Support Opportunities: Research Projects - Thematic Grants