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.