A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

Sean Knox; Tianhua Chen; Pan Su; Grigoris Antoniou

doi:10.1007/978-3-030-86993-9_38

A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

Sean Knox, Tianhua Chen, Pan Su, Grigoris Antoniou

North China Electric Power University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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%.

Original language	English
Title of host publication	Brain Informatics
Subtitle of host publication	14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings
Editors	Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
Place of Publication	Cham
Publisher	Springer, Cham
Pages	423-432
Number of pages	10
Volume	12960
Edition	1st
ISBN (Electronic)	9783030869939
ISBN (Print)	9783030869922
DOIs	https://doi.org/10.1007/978-3-030-86993-9_38
Publication status	Published - 15 Sept 2021
Event	14th 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 Sept 2021 → 19 Sept 2021 Conference number: 14 https://www.bi2021.org/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12960 LNCS/LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Brain Informatics
Abbreviated title	BI 2021
Country/Territory	Italy
City	Padova - virtually
Period	17/09/21 → 19/09/21
Internet address	https://www.bi2021.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1007/978-3-030-86993-9_38Licence: Unspecified

PhD___Alzheimer_s_DetectionAccepted author manuscript, 433 KBLicence: Unspecified

Cite this

Knox, S., Chen, T., Su, P., & Antoniou, G. (2021). A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease. In M. Mahmud, M. S. Kaiser, S. Vassanelli, Q. Dai, & N. Zhong (Eds.), Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings (1st ed., Vol. 12960, pp. 423-432). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNCS/LNAI). Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_38

Knox, Sean ; Chen, Tianhua ; Su, Pan et al. / A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease. Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. editor / Mufti Mahmud ; M Shamim Kaiser ; Stefano Vassanelli ; Qionghai Dai ; Ning Zhong. Vol. 12960 1st. ed. Cham : Springer, Cham, 2021. pp. 423-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{377baf2bd6c1456289e8cf0672957f1b,

title = "A Parallel Machine Learning Framework for Detecting Alzheimer{\textquoteright}s Disease",

abstract = "This paper proposes a parallel machine learning framework for detecting Alzheimer{\textquoteright}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 Na{\"i}ve Bayes classifier where sensitivity/specificity achieved were 80\%/81\% respectively and a balanced accuracy score of 80\%.",

keywords = "Alzheimer{\textquoteright}s disease, Autoencoder, Gaussian Na{\"i}ve Bayes, Machine learning, Multi-layer perceptron, Support vector machine",

author = "Sean Knox and Tianhua Chen and Pan Su and Grigoris Antoniou",

note = "Funding Information: Acknowledgements. The authors are grateful to the reviewers for their constructive comments, which have helped elevate this piece of work significantly. The first author would also like to thank the University of Huddersfield for providing the PhD scholarship in support of their research. Part of this piece of work is also supported by the Santander International Research Funding Scheme and National Natural Science Foundation of China under grant 61906181. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 14th International Conference on Brain Informatics : Innovation Computational Approaches for Understanding Brain Functions and Treat its Disorders, BI 2021 ; Conference date: 17-09-2021 Through 19-09-2021",

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language = "English",

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editor = "Mufti Mahmud and Kaiser, \{M Shamim\} and Stefano Vassanelli and Qionghai Dai and Ning Zhong",

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Knox, S, Chen, T, Su, P & Antoniou, G 2021, A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease. in M Mahmud, MS Kaiser, S Vassanelli, Q Dai & N Zhong (eds), Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. 1st edn, vol. 12960, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12960 LNCS/LNAI, Springer, Cham, Cham, pp. 423-432, 14th International Conference on Brain Informatics, Padova - virtually, Italy, 17/09/21. https://doi.org/10.1007/978-3-030-86993-9_38

A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease. / Knox, Sean; Chen, Tianhua; Su, Pan et al.
Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. ed. / Mufti Mahmud; M Shamim Kaiser; Stefano Vassanelli; Qionghai Dai; Ning Zhong. Vol. 12960 1st. ed. Cham: Springer, Cham, 2021. p. 423-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNCS/LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

AU - Knox, Sean

AU - Chen, Tianhua

AU - Su, Pan

AU - Antoniou, Grigoris

N1 - Conference code: 14

PY - 2021/9/15

Y1 - 2021/9/15

N2 - 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%.

AB - 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%.

KW - Alzheimer’s disease

KW - Autoencoder

KW - Gaussian Naïve Bayes

KW - Machine learning

KW - Multi-layer perceptron

KW - Support vector machine

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UR - https://link.springer.com/book/10.1007/978-3-030-86993-9

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DO - 10.1007/978-3-030-86993-9_38

M3 - Conference contribution

AN - SCOPUS:85115837784

SN - 9783030869922

VL - 12960

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 423

EP - 432

BT - Brain Informatics

A2 - Mahmud, Mufti

A2 - Kaiser, M Shamim

A2 - Vassanelli, Stefano

A2 - Dai, Qionghai

A2 - Zhong, Ning

PB - Springer, Cham

CY - Cham

T2 - 14th International Conference on Brain Informatics

Y2 - 17 September 2021 through 19 September 2021

ER -

Knox S, Chen T, Su P, Antoniou G. A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease. In Mahmud M, Kaiser MS, Vassanelli S, Dai Q, Zhong N, editors, Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. 1st ed. Vol. 12960. Cham: Springer, Cham. 2021. p. 423-432. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2021 Sept 15. doi: 10.1007/978-3-030-86993-9_38

A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

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