Comparison of convolutional neural networks approaches applied to the diagnosis of Alzheimer’s disease

Alzheimer's disease (AD), a neurodegenerative disorder, progressively impairs memory and cognitive functions.Magnetic resonance imaging (MRI) is used as AD diagnosis and progress monitoring method.Convolutional Neural Network (CNN) is a data-driven deep learning model containing layers transforming data input using convolution filters.The goal of this paper is to present an analysis of the CNN architectures for classifying AD diagnoses using functional brain MRI scans acquired by the experimental dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI).Results show CNNs variants such as InceptionV3 and In-ceptionResNetV2 as powerful computational tools for developing predictive neuroimaging biomarkers in AD diagnosis applications, with accuracy above 70%.