A Parallel Machine Learning Framework for Detecting Alzheimer's Disease

Sean Knox, Tianhua Chen, Pan Su, Grigoris Antoniou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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%.
Original languageEnglish
Title of host publicationProceedings for the 14th International Conference on Brain Informatics (BI 2021)
EditorsMulti Mahmud, M. Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Jack Lutz
PublisherSpringer International Publishing AG
Number of pages10
ISBN (Electronic)9783030869939
ISBN (Print)9783030869922
Publication statusPublished - 16 Sep 2021
Event14th International Conference on Brain Informatics: Innovation Computational Approaches for Understanding Brain Functions and Treat its Disorders - Virtual conference due to COVID-19, Padova - virtually, Italy
Duration: 17 Sep 202119 Sep 2021
Conference number: 14

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer International Publishing AG


Conference14th International Conference on Brain Informatics
Abbreviated titleBI 2021
CityPadova - virtually
Internet address


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