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 important f...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2021/6690539 |
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| author | Fanar E. K. Al-Khuzaie Oguz Bayat Adil D. Duru |
| author_facet | Fanar E. K. Al-Khuzaie Oguz Bayat Adil D. Duru |
| author_sort | Fanar E. K. Al-Khuzaie |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-28df05107ee24e92a74d3347ec29073e |
| institution | OA Journals |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-28df05107ee24e92a74d3347ec29073e2025-08-20T02:03:54ZengWileyApplied Bionics and Biomechanics1176-23221754-21032021-01-01202110.1155/2021/66905396690539Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural NetworkFanar E. K. Al-Khuzaie0Oguz Bayat1Adil D. Duru2Graduate School of Science and Engineering, Altinbas University, Istanbul, TurkeyGraduate School of Science and Engineering, Altinbas University, Istanbul, TurkeyDepartment of Physical Education and Sports Teaching, University of Marmara, Istanbul, TurkeyThere 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.http://dx.doi.org/10.1155/2021/6690539 |
| spellingShingle | Fanar E. K. Al-Khuzaie Oguz Bayat Adil D. Duru Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network Applied Bionics and Biomechanics |
| 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 |
| url | http://dx.doi.org/10.1155/2021/6690539 |
| work_keys_str_mv | AT fanarekalkhuzaie diagnosisofalzheimerdiseaseusing2dmrislicesbyconvolutionalneuralnetwork AT oguzbayat diagnosisofalzheimerdiseaseusing2dmrislicesbyconvolutionalneuralnetwork AT adildduru diagnosisofalzheimerdiseaseusing2dmrislicesbyconvolutionalneuralnetwork |