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2021 | OriginalPaper | Chapter

A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

Authors : Sean A. Knox, Tianhua Chen, Pan Su, Grigoris Antoniou

Published in: Brain Informatics

Publisher: Springer International Publishing

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Abstract

This paper proposes a parallel machine learning framework for detecting Alzheimer’s disease through T1-weighted MRI scans localised to the hippocampus, segmented between the left and right hippocampi. Feature extraction is first performed by 2 separately trained, unsupervised learning based AutoEncoders, where the left and right hippocampi are fed into their respective AutoEncoder. Classification is then performed by a pair of classifiers on the encoded data from the AutoEncoders, to which each pair of the classifiers are aggregated together using a soft voting ensemble process. The best averaged aggregated model results recorded was with the Gaussian Naïve Bayes classifier where sensitivity/specificity achieved were 80%/81% respectively and a balanced accuracy score of 80%.
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Metadata
Title
A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease
Authors
Sean A. Knox
Tianhua Chen
Pan Su
Grigoris Antoniou
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-86993-9_38

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