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Abstract #3050

An extended-2D CNN approach for diagnosis of Alzheimer’s disease through structural MRI

Mariana Pereira1, Roberto Lotufo1, and Leticia Rittner1

1Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Campinas, Brazil

Alzheimer's disease (AD) is a devastating type of dementia that affects millions of people around the world. To date, there is no cure for Alzheimer's and its early-diagnosis has been a challenging task. The current techniques for AD diagnosis have explored the structural information of MRI. The aim of this work is to investigate the use of 2D-CNN approaches to distinguish AD patients from MCI and NC using T1-weighted MRI, since most of the works either explored the classic machine-learning or 3D-CNN approaches. The main novelty of our methodology is the use of an extended-2D approach, which explores the volumetric information of the MRI data while maintaining the low costs associated with a 2D approach.

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