A Deep Learning Pipeline for Classifying Different Stages of Alzheimer's Disease from fMRI Data.

Abstract Alzheimer’s disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of AD. However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers. In this thesis, convolutional neuronal network architecture AlexNet was applied to fMRI datasets to classify different stages of the disease. We classified five different stages of Alzheimer’s using a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer’s disease (AD). The model was implemented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed to avoid any noise. Then, low to high level features were extracted and learned using the AlexNet model. Our experiments show significant improvement in classification. The average accuracy of the model was 97.63%. We then tested our model on test datasets to evaluate the accuracy of the model per class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, 98.34% for NC, and 94.55% for SMC.

Name thesis:degree_name

M.Sc. Computer Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Mathematics and Science

Department dc:contributor.department

Department of Computer Science

Grantor

Brock University

Year dc:date.issued

2018

Author dc:creator

• kazemi, yosra

• Deep Learning, fMRI, Classification, Alzheimer's Disease, Machine Learning

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/13163

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/13163