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Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI

Alzheimer's disease (AD) is a neurodegenerative ailment that causes cognitive deterioration due to changes in brain structure. Individuals usually see diagnostic symptoms after irreversible brain damage has occurred. In order to slow the course of the illness and enhance the quality of life for...

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Main Authors: Kadhim, Karrar A., Mohamed, Farhan, Sakran, Ammar AbdRaba, Adnan, Myasar Mundher, Salman, Ghalib Ahmed
Format: Article
Language:English
Published: Penerbit UTM Press 2023
Subjects:
Q Science (General)
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/105330/1/KarrarAKadhim2023_EarlyDiagnosisofAlzheimersDisease.pdf
http://eprints.utm.my/105330/
http://dx.doi.org/10.11113/mjfas.v19n3.2908
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spelling my.utm.1053302024-04-22T10:26:40Z http://eprints.utm.my/105330/ Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI Kadhim, Karrar A. Mohamed, Farhan Sakran, Ammar AbdRaba Adnan, Myasar Mundher Salman, Ghalib Ahmed Q Science (General) QA75 Electronic computers. Computer science Alzheimer's disease (AD) is a neurodegenerative ailment that causes cognitive deterioration due to changes in brain structure. Individuals usually see diagnostic symptoms after irreversible brain damage has occurred. In order to slow the course of the illness and enhance the quality of life for AD patients, early diagnosis is crucial. Recent advances in machine learning and scanning have made the use of these methods to detect AD in its earliest stages possible. This article uses deep learning using CNN methods to extract picture characteristics from ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets to improve Alzheimer's disease diagnosis techniques. This descriptor will be used in conjunction with the CNN to categorize the illness and add new characteristics that are more accurate, quicker, and stable than the current features. In this process, an Alzheimer's detection System will be implemented to mitigate the adverse effects of data imbalance on recognition performance, and an integrated multi-depth architectural technology will be introduced to boost recognition quality. Using the suggested model of the convolution neural network (CNN) technique, classification accuracy results were obtained above 97%. Penerbit UTM Press 2023-01 Article PeerReviewed application/pdf en http://eprints.utm.my/105330/1/KarrarAKadhim2023_EarlyDiagnosisofAlzheimersDisease.pdf Kadhim, Karrar A. and Mohamed, Farhan and Sakran, Ammar AbdRaba and Adnan, Myasar Mundher and Salman, Ghalib Ahmed (2023) Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI. Malaysian Journal of Fundamental and Applied Sciences, 19 (3). pp. 362-368. ISSN 2289-599X http://dx.doi.org/10.11113/mjfas.v19n3.2908 DOI:10.11113/mjfas.v19n3.2908
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
QA75 Electronic computers. Computer science
spellingShingle Q Science (General)
QA75 Electronic computers. Computer science
Kadhim, Karrar A.
Mohamed, Farhan
Sakran, Ammar AbdRaba
Adnan, Myasar Mundher
Salman, Ghalib Ahmed
Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI
description Alzheimer's disease (AD) is a neurodegenerative ailment that causes cognitive deterioration due to changes in brain structure. Individuals usually see diagnostic symptoms after irreversible brain damage has occurred. In order to slow the course of the illness and enhance the quality of life for AD patients, early diagnosis is crucial. Recent advances in machine learning and scanning have made the use of these methods to detect AD in its earliest stages possible. This article uses deep learning using CNN methods to extract picture characteristics from ADNI (Alzheimer's Disease Neuroimaging Initiative) datasets to improve Alzheimer's disease diagnosis techniques. This descriptor will be used in conjunction with the CNN to categorize the illness and add new characteristics that are more accurate, quicker, and stable than the current features. In this process, an Alzheimer's detection System will be implemented to mitigate the adverse effects of data imbalance on recognition performance, and an integrated multi-depth architectural technology will be introduced to boost recognition quality. Using the suggested model of the convolution neural network (CNN) technique, classification accuracy results were obtained above 97%.
format Article
author Kadhim, Karrar A.
Mohamed, Farhan
Sakran, Ammar AbdRaba
Adnan, Myasar Mundher
Salman, Ghalib Ahmed
author_facet Kadhim, Karrar A.
Mohamed, Farhan
Sakran, Ammar AbdRaba
Adnan, Myasar Mundher
Salman, Ghalib Ahmed
author_sort Kadhim, Karrar A.
title Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI
title_short Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI
title_full Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI
title_fullStr Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI
title_full_unstemmed Early diagnosis of Alzheimer's disease using convolutional Neural Network-Based MRI
title_sort early diagnosis of alzheimer's disease using convolutional neural network-based mri
publisher Penerbit UTM Press
publishDate 2023
url http://eprints.utm.my/105330/1/KarrarAKadhim2023_EarlyDiagnosisofAlzheimersDisease.pdf
http://eprints.utm.my/105330/
http://dx.doi.org/10.11113/mjfas.v19n3.2908
_version_ 1797905998378172416
score 13.211869

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