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GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network

There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's dis...

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Autores principales: Amini, Morteza, Pedram, Mir Mohsen, Moradi, AliReza, Jamshidi, Mahdieh, Ouchani, Mahshad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381254/
https://www.ncbi.nlm.nih.gov/pubmed/35983158
http://dx.doi.org/10.1155/2022/7413081
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author Amini, Morteza
Pedram, Mir Mohsen
Moradi, AliReza
Jamshidi, Mahdieh
Ouchani, Mahshad
author_facet Amini, Morteza
Pedram, Mir Mohsen
Moradi, AliReza
Jamshidi, Mahdieh
Ouchani, Mahshad
author_sort Amini, Morteza
collection PubMed
description There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.
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spelling pubmed-93812542022-08-17 GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network Amini, Morteza Pedram, Mir Mohsen Moradi, AliReza Jamshidi, Mahdieh Ouchani, Mahshad Comput Intell Neurosci Research Article There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy. Hindawi 2022-08-09 /pmc/articles/PMC9381254/ /pubmed/35983158 http://dx.doi.org/10.1155/2022/7413081 Text en Copyright © 2022 Morteza Amini et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Amini, Morteza
Pedram, Mir Mohsen
Moradi, AliReza
Jamshidi, Mahdieh
Ouchani, Mahshad
GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network
title GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network
title_full GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network
title_fullStr GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network
title_full_unstemmed GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network
title_short GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network
title_sort gc-cnnnet: diagnosis of alzheimer's disease with pet images using genetic and convolutional neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381254/
https://www.ncbi.nlm.nih.gov/pubmed/35983158
http://dx.doi.org/10.1155/2022/7413081
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