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Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network
There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer's disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most import...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872776/ https://www.ncbi.nlm.nih.gov/pubmed/33623535 http://dx.doi.org/10.1155/2021/6690539 |
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author | Al-Khuzaie, Fanar E. K. Bayat, Oguz Duru, Adil D. |
author_facet | Al-Khuzaie, Fanar E. K. Bayat, Oguz Duru, Adil D. |
author_sort | Al-Khuzaie, Fanar E. K. |
collection | PubMed |
description | There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer's disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer's patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer's disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer's patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet. |
format | Online Article Text |
id | pubmed-7872776 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-78727762021-02-22 Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network Al-Khuzaie, Fanar E. K. Bayat, Oguz Duru, Adil D. Appl Bionics Biomech Research Article There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer's disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer's patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer's disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer's patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet. Hindawi 2021-02-02 /pmc/articles/PMC7872776/ /pubmed/33623535 http://dx.doi.org/10.1155/2021/6690539 Text en Copyright © 2021 Fanar E. K. Al-Khuzaie 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 Al-Khuzaie, Fanar E. K. Bayat, Oguz Duru, Adil D. Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network |
title | Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network |
title_full | Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network |
title_fullStr | Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network |
title_full_unstemmed | Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network |
title_short | Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network |
title_sort | diagnosis of alzheimer disease using 2d mri slices by convolutional neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7872776/ https://www.ncbi.nlm.nih.gov/pubmed/33623535 http://dx.doi.org/10.1155/2021/6690539 |
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