Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks

ABSTRACTAlzheimer's disease (AD) is a neurological condition that impairs the patient's cognitive function. The article presents a deep learning architecture using the implicit image from MRI to categories MRI scans and detect AD on time. The annotated data is divided into patients with AD...

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Main Authors: Yinjun Zhang, Lingzhi Wang
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Connection Science
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/09540091.2024.2321351
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author Yinjun Zhang
Lingzhi Wang
author_facet Yinjun Zhang
Lingzhi Wang
author_sort Yinjun Zhang
collection DOAJ
description ABSTRACTAlzheimer's disease (AD) is a neurological condition that impairs the patient's cognitive function. The article presents a deep learning architecture using the implicit image from MRI to categories MRI scans and detect AD on time. The annotated data is divided into patients with AD and control groups. After GAN creates the synthetic and real images, the dataset is passed through CNN to detect spatial features from the scans. We used 30 slices from the region of the top brain above the eyes for learning. In order to train the CNNs and evaluate the results, the data is divided using the 10-fold cross-validation evaluating technique to validate the model, the accuracy estimates are 99.67%, 98.76%, respectively.
format Article
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institution OA Journals
issn 0954-0091
1360-0494
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publishDate 2024-12-01
publisher Taylor & Francis Group
record_format Article
series Connection Science
spelling doaj-art-6d6752ba3e804ce4a1fe95cfd4605dad2025-08-20T02:06:12ZengTaylor & Francis GroupConnection Science0954-00911360-04942024-12-0136110.1080/09540091.2024.2321351Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networksYinjun Zhang0Lingzhi Wang1Guangxi Science and Technology Normal University, Laibin, ChinaGuangxi Science and Technology Normal University, Laibin, ChinaABSTRACTAlzheimer's disease (AD) is a neurological condition that impairs the patient's cognitive function. The article presents a deep learning architecture using the implicit image from MRI to categories MRI scans and detect AD on time. The annotated data is divided into patients with AD and control groups. After GAN creates the synthetic and real images, the dataset is passed through CNN to detect spatial features from the scans. We used 30 slices from the region of the top brain above the eyes for learning. In order to train the CNNs and evaluate the results, the data is divided using the 10-fold cross-validation evaluating technique to validate the model, the accuracy estimates are 99.67%, 98.76%, respectively.https://www.tandfonline.com/doi/10.1080/09540091.2024.2321351Alzheimer’s diseaseMRI image datadual GAN and pyramid networksdeep learning
spellingShingle Yinjun Zhang
Lingzhi Wang
Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks
Connection Science
Alzheimer’s disease
MRI image data
dual GAN and pyramid networks
deep learning
title Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks
title_full Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks
title_fullStr Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks
title_full_unstemmed Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks
title_short Early diagnosis of Alzheimer’s disease using dual GAN model with pyramid attention networks
title_sort early diagnosis of alzheimer s disease using dual gan model with pyramid attention networks
topic Alzheimer’s disease
MRI image data
dual GAN and pyramid networks
deep learning
url https://www.tandfonline.com/doi/10.1080/09540091.2024.2321351
work_keys_str_mv AT yinjunzhang earlydiagnosisofalzheimersdiseaseusingdualganmodelwithpyramidattentionnetworks
AT lingzhiwang earlydiagnosisofalzheimersdiseaseusingdualganmodelwithpyramidattentionnetworks