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

4 Citations (Scopus)


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 publicationBrain Informatics
Subtitle of host publication14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings
EditorsMufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
Place of PublicationCham
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783030869939
ISBN (Print)9783030869922
Publication statusPublished - 15 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 Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12960 LNCS/LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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


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