Journal of Intelligent Systems and Internet of Things

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https://doi.org/10.54216/JISIoT

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2690-6791ISSN (Online) 2769-786XISSN (Print)

Volume 16 , Issue 2 , PP: 202-213, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Alzheimer Detection Using Deep Learning Methods

Raghad K. Mohammed 1 , Mohammed Q. Jawad 2 , Othman Mohammed Jasim 3 *

  • 1 Department of Basic Science, College of Dentistry, University of Baghdad, Iraq - (raghad_meme@codental.uobaghdad.edu.iq)
  • 2 Uuniversity of information Technology and Communication, Biomedical Informatics College, Baghdad, Iraq - (mohammed.qassim2002@uoitc.edu.iq)
  • 3 Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq - (othmanmohmmed45@gmail.com)
  • Doi: https://doi.org/10.54216/JISIoT.160215

    Received: January 17, 2025 Revised: February 14, 2025 Accepted: March 12, 2025
    Abstract

    This study proposes a deep learning-based framework to detect and classify Alzheimer's disease (AD) in the early stages using medical imaging, and specifically Magnetic Resonance Imaging (MRI). Specifically, we propose a Convolution Neural Network (CNN) based model and transfer learn (MobileNet) through pre-trained models based on task domain to improve model performance on binary AD classification. Thanks to minimizing computational complexity and memory costs, the model with 99.86% accuracy rate can mitigate overfitting and is an ideal approach for real time and eco-friendly monitoring of AD evolution. The findings suggest that the model could help clinicians in diagnosing AD even based on MRI images, which has great potential as a scalable and efficient solution for the early-stage diagnosis and classification of the disease. Our work will include the addition of further pre-trained models, increased dataset size via data augmentation, and the application of MRI segmentation to better isolate some of the key features of Alzheimer.

    Keywords :

    Deep learning , Machine Learning , Alzheimer Detection , MobileNet , Convolution Neural Network

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    Cite This Article As :
    K., Raghad. , Q., Mohammed. , Mohammed, Othman. Alzheimer Detection Using Deep Learning Methods. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2025, pp. 202-213. DOI: https://doi.org/10.54216/JISIoT.160215
    K., R. Q., M. Mohammed, O. (2025). Alzheimer Detection Using Deep Learning Methods. Journal of Intelligent Systems and Internet of Things, (), 202-213. DOI: https://doi.org/10.54216/JISIoT.160215
    K., Raghad. Q., Mohammed. Mohammed, Othman. Alzheimer Detection Using Deep Learning Methods. Journal of Intelligent Systems and Internet of Things , no. (2025): 202-213. DOI: https://doi.org/10.54216/JISIoT.160215
    K., R. , Q., M. , Mohammed, O. (2025) . Alzheimer Detection Using Deep Learning Methods. Journal of Intelligent Systems and Internet of Things , () , 202-213 . DOI: https://doi.org/10.54216/JISIoT.160215
    K. R. , Q. M. , Mohammed O. [2025]. Alzheimer Detection Using Deep Learning Methods. Journal of Intelligent Systems and Internet of Things. (): 202-213. DOI: https://doi.org/10.54216/JISIoT.160215
    K., R. Q., M. Mohammed, O. "Alzheimer Detection Using Deep Learning Methods," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 202-213, 2025. DOI: https://doi.org/10.54216/JISIoT.160215