Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗

Noushath Shaffi; Vimbi Viswan; Mufti Mahmud; Faizal Hajamohideen; Karthikeyan Subramanian

doi:10.1109/WI-IAT59888.2023.00082

Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗

Noushath Shaffi
, Vimbi Viswan
, Mufti Mahmud
, Faizal Hajamohideen
, Karthikeyan Subramanian

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

13 Scopus citations

Abstract

While most contemporary algorithms typically utilize MRI data from a single plane, this study highlights the importance of incorporating multiplanar MRI features for enhanced performance. Specifically, tree-based machine learning algorithms were employed to compare the accuracy of individual plane analysis versus a multiplanar approach using the popular ADNI dataset. The results unequivocally demonstrate that the multiplanar approach consistently outperforms any single plane analysis in terms of classification accuracy for any given algorithm. These findings provide evidence-based results supporting the integration of multiplanar MRI features to achieve improved performance in MRI-based classification tasks. A significant improvement in accuracy of 8-10% is achieved by the utilization of multiplanar MRI features as against the single plane.

Original language	English
Title of host publication	Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	496-502
Number of pages	7
ISBN (Electronic)	9798350309188
DOIs	https://doi.org/10.1109/WI-IAT59888.2023.00082
State	Published - 2023
Externally published	Yes
Event	22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023 - Hybrid, Venice, Italy Duration: 26 Oct 2023 → 29 Oct 2023

Publication series

Name	Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023

Conference

Conference	22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023
Country/Territory	Italy
City	Hybrid, Venice
Period	26/10/23 → 29/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

AdaBoost
Alzheimer's Disease
Decision Tree
Ensemble
Machine Learning
Multiplanar MRI
Random Forest
XGBoost

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/WI-IAT59888.2023.00082

Cite this

Shaffi, N., Viswan, V., Mahmud, M., Hajamohideen, F., & Subramanian, K. (2023). Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗. In Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023 (pp. 496-502). (Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WI-IAT59888.2023.00082

Shaffi, Noushath ; Viswan, Vimbi ; Mahmud, Mufti et al. / Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗. Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 496-502 (Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023).

@inproceedings{cb31f76c176746d0b458c49707301c7b,

title = "Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗",

abstract = "While most contemporary algorithms typically utilize MRI data from a single plane, this study highlights the importance of incorporating multiplanar MRI features for enhanced performance. Specifically, tree-based machine learning algorithms were employed to compare the accuracy of individual plane analysis versus a multiplanar approach using the popular ADNI dataset. The results unequivocally demonstrate that the multiplanar approach consistently outperforms any single plane analysis in terms of classification accuracy for any given algorithm. These findings provide evidence-based results supporting the integration of multiplanar MRI features to achieve improved performance in MRI-based classification tasks. A significant improvement in accuracy of 8-10\% is achieved by the utilization of multiplanar MRI features as against the single plane.",

keywords = "AdaBoost, Alzheimer's Disease, Decision Tree, Ensemble, Machine Learning, Multiplanar MRI, Random Forest, XGBoost",

author = "Noushath Shaffi and Vimbi Viswan and Mufti Mahmud and Faizal Hajamohideen and Karthikeyan Subramanian",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023 ; Conference date: 26-10-2023 Through 29-10-2023",

year = "2023",

doi = "10.1109/WI-IAT59888.2023.00082",

language = "English",

series = "Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "496--502",

booktitle = "Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023",

address = "United States",

}

Shaffi, N, Viswan, V, Mahmud, M, Hajamohideen, F & Subramanian, K 2023, Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗. in Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023. Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023, Institute of Electrical and Electronics Engineers Inc., pp. 496-502, 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023, Hybrid, Venice, Italy, 26/10/23. https://doi.org/10.1109/WI-IAT59888.2023.00082

Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗. / Shaffi, Noushath; Viswan, Vimbi; Mahmud, Mufti et al.
Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 496-502 (Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023).

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

TY - GEN

T1 - Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗

AU - Shaffi, Noushath

AU - Viswan, Vimbi

AU - Mahmud, Mufti

AU - Hajamohideen, Faizal

AU - Subramanian, Karthikeyan

PY - 2023

Y1 - 2023

N2 - While most contemporary algorithms typically utilize MRI data from a single plane, this study highlights the importance of incorporating multiplanar MRI features for enhanced performance. Specifically, tree-based machine learning algorithms were employed to compare the accuracy of individual plane analysis versus a multiplanar approach using the popular ADNI dataset. The results unequivocally demonstrate that the multiplanar approach consistently outperforms any single plane analysis in terms of classification accuracy for any given algorithm. These findings provide evidence-based results supporting the integration of multiplanar MRI features to achieve improved performance in MRI-based classification tasks. A significant improvement in accuracy of 8-10% is achieved by the utilization of multiplanar MRI features as against the single plane.

AB - While most contemporary algorithms typically utilize MRI data from a single plane, this study highlights the importance of incorporating multiplanar MRI features for enhanced performance. Specifically, tree-based machine learning algorithms were employed to compare the accuracy of individual plane analysis versus a multiplanar approach using the popular ADNI dataset. The results unequivocally demonstrate that the multiplanar approach consistently outperforms any single plane analysis in terms of classification accuracy for any given algorithm. These findings provide evidence-based results supporting the integration of multiplanar MRI features to achieve improved performance in MRI-based classification tasks. A significant improvement in accuracy of 8-10% is achieved by the utilization of multiplanar MRI features as against the single plane.

KW - AdaBoost

KW - Alzheimer's Disease

KW - Decision Tree

KW - Ensemble

KW - Machine Learning

KW - Multiplanar MRI

KW - Random Forest

KW - XGBoost

UR - https://www.scopus.com/pages/publications/85182523109

U2 - 10.1109/WI-IAT59888.2023.00082

DO - 10.1109/WI-IAT59888.2023.00082

M3 - Conference contribution

AN - SCOPUS:85182523109

T3 - Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023

SP - 496

EP - 502

BT - Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023

Y2 - 26 October 2023 through 29 October 2023

ER -

Shaffi N, Viswan V, Mahmud M, Hajamohideen F, Subramanian K. Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗. In Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 496-502. (Proceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023). doi: 10.1109/WI-IAT59888.2023.00082

Multi-Planar MRI-Based Classification of Alzheimer's Disease Using Tree-Based Machine Learning Algorithms∗

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this