Ghazal, T M, Abbas, S, Munir, S, A. Khan, M, Ahmad, M, Issa, G, Binish Zahra, S, Adnan Khan, M and Kamrul Hasan, M (2022)Alzheimer Disease Detection Empowered with Transfer Learning. Computers, Materials & Continua, 70 (3). pp. 5005-5019. ISSN 1546-2226
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Abstract
Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.
Affiliation: | Skyline University College |
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SUC Author(s): | Ghazal, T M ORCID: https://orcid.org/0000-0003-0672-7924 and Issa, G |
All Author(s): | Ghazal, T M, Abbas, S, Munir, S, A. Khan, M, Ahmad, M, Issa, G, Binish Zahra, S, Adnan Khan, M and Kamrul Hasan, M |
Item Type: | Article |
Uncontrolled Keywords: | Convolutional neural network (CNN); alzheimer's disease (AD); medical resonance imagining; mild demented |
Subjects: | B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 13 Apr 2022 09:04 |
Last Modified: | 24 Jun 2022 11:50 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/157 |
Publisher URL: | https://doi.org/10.32604/cmc.2022.020866 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/37365 |
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