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Alzheimer Disease Detection Empowered with Transfer Learning

Taher M. Ghazal1,2, Sagheer Abbas3, Sundus Munir3,4, M. A. Khan5, Munir Ahmad3, Ghassan F. Issa2, Syeda Binish Zahra4, Muhammad Adnan Khan6,*, Mohammad Kamrul Hasan1

1 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia
2 School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE
3 School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
4 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
5 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan
6 Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, Korea

* Corresponding Author: Muhammad Adnan Khan. Email: email

Computers, Materials & Continua 2022, 70(3), 5005-5019.


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.


Cite This Article

APA Style
Ghazal, T.M., Abbas, S., Munir, S., Khan, M.A., Ahmad, M. et al. (2022). Alzheimer disease detection empowered with transfer learning. Computers, Materials & Continua, 70(3), 5005-5019.
Vancouver Style
Ghazal TM, Abbas S, Munir S, Khan MA, Ahmad M, Issa GF, et al. Alzheimer disease detection empowered with transfer learning. Comput Mater Contin. 2022;70(3):5005-5019
IEEE Style
T.M. Ghazal et al., "Alzheimer Disease Detection Empowered with Transfer Learning," Comput. Mater. Contin., vol. 70, no. 3, pp. 5005-5019. 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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