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An Intelligent Method of Alzheimer's disease Using Deep learning
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Firouzeh Razavi1  , Mohammad Jafar Tarokh * 2, Mahmood Alborzi3 |
1- Ph.D. Student, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran,Iran
2- Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran. , Mjtarokh@kntu.ac.ir
3- Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran. |
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Abstract: (2719 Views) |
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. |
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| Keywords: Alzheimer's disease, Deep Learning Neural Network, Classification |
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Full-Text [PDF 1449 kb]
(1518 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2020/07/25 | Accepted: 2020/09/21 | Published: 2020/09/21
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