An Intelligent Method of Alzheimer\'s disease Using Deep learning
Aim and Background: Alzheimer 's disease is the most common form of dementia which has caused disorder in memory. Cerebral palsy and posttraumatic stress disorder (veterans of war, warriors, armed forces) play an important role in increasing the risk of Alzheimer's disease. The nature of t...
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| Format: | Article |
| Language: | fas |
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Armaqan Danesh Firoozeh
2020-07-01
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| Series: | Taḥqīqāt-i ̒Ulūm-i Raftārī |
| Subjects: | |
| Online Access: | http://rbs.mui.ac.ir/article-1-784-en.html |
| _version_ | 1797698143676006400 |
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| author | Firouzeh Razavi Mohammad Jafar Tarokh Mahmood Alborzi |
| author_facet | Firouzeh Razavi Mohammad Jafar Tarokh Mahmood Alborzi |
| author_sort | Firouzeh Razavi |
| collection | DOAJ |
| description | Aim and Background: Alzheimer 's disease is the most common form of dementia which has caused disorder in memory. Cerebral palsy and posttraumatic stress disorder (veterans of war, warriors, armed forces) play an important role in increasing the risk of Alzheimer's disease. The nature of the large dimensions of neural data, as well as the small number of available samples, make for an accurate computer diagnostic system. The aim of this study was to apply deep neural networks to develop an automatic disease diagnosis system.
Methods and Materials: In this research, studies on magnetic resonance imaging of war veterans are done by python Software. In the proposed model, in the proposed model, 10% of the images of the data base were selected for training. In the first stage, the training is from deep learning with Convolutional network to extract the features, then in the second stage, in order to classify the health status based on the learned features.
Findings: The results of the analysis are also compared with the results presented in previous Studies. The proposed method has higher detection accuracy than the existing Ones, which increases the accuracy of detection in many cases.
Conclusions: The results of this study showed that using intelligent methods based on deep learning can accurately diagnose the disease. |
| first_indexed | 2024-03-12T03:50:05Z |
| format | Article |
| id | doaj.art-f4cf17a52c6941d6ac4548d778727626 |
| institution | Directory Open Access Journal |
| issn | 1735-2029 2008-8248 |
| language | fas |
| last_indexed | 2024-03-12T03:50:05Z |
| publishDate | 2020-07-01 |
| publisher | Armaqan Danesh Firoozeh |
| record_format | Article |
| series | Taḥqīqāt-i ̒Ulūm-i Raftārī |
| spelling | doaj.art-f4cf17a52c6941d6ac4548d7787276262023-09-03T12:30:20ZfasArmaqan Danesh FiroozehTaḥqīqāt-i ̒Ulūm-i Raftārī1735-20292008-82482020-07-01182260269An Intelligent Method of Alzheimer\'s disease Using Deep learningFirouzeh Razavi0Mohammad Jafar Tarokh1Mahmood Alborzi2 Ph.D. Student, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran,Iran Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran. Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran. Aim and Background: Alzheimer 's disease is the most common form of dementia which has caused disorder in memory. Cerebral palsy and posttraumatic stress disorder (veterans of war, warriors, armed forces) play an important role in increasing the risk of Alzheimer's disease. The nature of the large dimensions of neural data, as well as the small number of available samples, make for an accurate computer diagnostic system. The aim of this study was to apply deep neural networks to develop an automatic disease diagnosis system. Methods and Materials: In this research, studies on magnetic resonance imaging of war veterans are done by python Software. In the proposed model, in the proposed model, 10% of the images of the data base were selected for training. In the first stage, the training is from deep learning with Convolutional network to extract the features, then in the second stage, in order to classify the health status based on the learned features. Findings: The results of the analysis are also compared with the results presented in previous Studies. The proposed method has higher detection accuracy than the existing Ones, which increases the accuracy of detection in many cases. Conclusions: The results of this study showed that using intelligent methods based on deep learning can accurately diagnose the disease.http://rbs.mui.ac.ir/article-1-784-en.htmlalzheimer's diseasedeep learning neural networkclassification |
| spellingShingle | Firouzeh Razavi Mohammad Jafar Tarokh Mahmood Alborzi An Intelligent Method of Alzheimer\'s disease Using Deep learning Taḥqīqāt-i ̒Ulūm-i Raftārī alzheimer's disease deep learning neural network classification |
| title | An Intelligent Method of Alzheimer\'s disease Using Deep learning |
| title_full | An Intelligent Method of Alzheimer\'s disease Using Deep learning |
| title_fullStr | An Intelligent Method of Alzheimer\'s disease Using Deep learning |
| title_full_unstemmed | An Intelligent Method of Alzheimer\'s disease Using Deep learning |
| title_short | An Intelligent Method of Alzheimer\'s disease Using Deep learning |
| title_sort | intelligent method of alzheimer s disease using deep learning |
| topic | alzheimer's disease deep learning neural network classification |
| url | http://rbs.mui.ac.ir/article-1-784-en.html |
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