Vol.70, No.3, 2022, pp.5005-5019, doi:10.32604/cmc.2022.020866
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:
Received 10 June 2021; Accepted 11 July 2021; Issue published 11 October 2021
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.
Convolutional neural network (CNN); alzheimer's disease (AD); medical resonance imagining; mild demented
Cite This Article
Ghazal, T. M., Abbas, S., Munir, S., Khan, M. A., Ahmad, M. et al. (2022). Alzheimer Disease Detection Empowered with Transfer Learning. CMC-Computers, Materials & Continua, 70(3), 5005–5019.
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.