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141248

Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering

Article

Last updated: 22 Jan 2023

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Abstract

Alzheimer's disease (AD) is a progressive brain disorder and a very common form of dementia. Neuroimaging techniques, such as Magnetic Resonance Imaging (MRI), produce detailed 3-dimensional images of the brain showing insights for amyloid deposits and inflammatory alterations as disease markers. The early diagnosis of AD using MRI provides a good chance for patients to prevent further brain deterioration by stopping the loss of nerve cells. This paper explores the use of unsupervised clustering approaches for the early diagnosis of AD. Though it is very common to use classification techniques for identifying medical diseases, the lack or the inaccuracies of labeled data can generate a problem. In this work, the k-means and k-medoids are compared while employing the Voxel Based Morphometry (VBM) features extracted from the MRI images. The effect of choosing certain local regions of interest (ROIs) for the analysis is also compared to the global whole-brain analysis. The results show that the proposed approach can perform an early diagnosis of AD with an accuracy of 76%.

DOI

10.21608/ijicis.2021.51180.1044

Keywords

unsupervised learning, Clustering, Regions of Interest (ROI), Alzheimer’s disease, Magnetic resonance imaging (MRI)

Authors

First Name

Yasmeen

Last Name

Farouk

MiddleName

-

Affiliation

Information Systems Departement, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Email

yfarouk.bakry@gmail.com

City

Cairo

Orcid

-

First Name

Sherine

Last Name

Rady

MiddleName

-

Affiliation

Ain Shams University

Email

srady@cis.asu.edu.eg

City

Cairo

Orcid

0000-0003-4991-966X

Volume

20

Article Issue

2

Related Issue

19789

Issue Date

2020-12-01

Receive Date

2020-11-26

Publish Date

2020-12-31

Page Start

112

Page End

124

Print ISSN

1687-109X

Online ISSN

2535-1710

Link

https://ijicis.journals.ekb.eg/article_141248.html

Detail API

https://ijicis.journals.ekb.eg/service?article_code=141248

Order

15

Type

Original Article

Type Code

494

Publication Type

Journal

Publication Title

International Journal of Intelligent Computing and Information Sciences

Publication Link

https://ijicis.journals.ekb.eg/

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Details

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Article

Created At

22 Jan 2023