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 1 , PP: 223-232, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

An Optimized Convolutional Neural Network for Alzheimer’s disease Detection

Amena Mahmoud 1 , Abdulaziz Shehab 2 * , A. S. Abohamama 3 , Esraa Al-Ezaly 4

  • 1 Information Systems Department, Faculty of Computer and Information, Kafr Elsheikh University, 33511, Egypt - (amena_mahmoud@fci.kfs.edu.eg)
  • 2 Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sa-kaka 72388, Saudi Arabia; Information Systems Department, Faculty of Computer and Information, Mansoura University, Mansoura, 35516, Egypt - (abdulaziz_shehab@mans.edu.eg)
  • 3 Department of Computer Science, Mansoura University, Mansoura 35516, Egypt; Department of Computer Science, Arab East Colleges, Riyadh 53354, Saudi Arabia - (abohamama@mans.edu.eg)
  • 4 Information Systems Department, Faculty of Computer and Information, Mansoura University, Mansoura, 35516, Egypt - (esraagamal@mans.edu.eg)
  • Doi: https://doi.org/10.54216/JISIoT.160119

    Received: October 17, 2024 Revised: January 03, 2025 Accepted: February 05, 2025
    Abstract

    Alzheimer’s disease (AD) is a serious diseases distressing society. AD is a complex disease associated with many risk factors, such as aging, genetics, head trauma, and vascular disease. AD is also influenced by environmental factors such as heavy metals and trace metals. The pathology of AD, including amyloid-peptide (Aβ) protein, neurofibrillary tangles (NFTs), and synaptic loss, is still unknown. There are many explanations for the causes of AD. Cholinergic dysfunction is a main danger factor for Alzheimer's disease, whereas others believe that abnormalities in the production and treating of Aβ protein are the primary cause. However, there is currently no accepted hypothesis explaining the pathogenesis of AD. Magnetic resonance imaging is used to diagnose Alzheimer's disease. Our new AD pathogenesis showed 99.77% accuracy with 0.2% efficiency loss and outperformed VGG16, MobileNet2, and Inception V3 without the Adam optimizer and folder hierarchy.

    Keywords :

    Alzheimer's ,   , Magnetic  , resonance ,   , Alzheimer&rsquo , s  , disease  , detection , Brain  , disease

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    Cite This Article As :
    Mahmoud, Amena. , Shehab, Abdulaziz. , S., A.. , Al-Ezaly, Esraa. An Optimized Convolutional Neural Network for Alzheimer’s disease Detection. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2025, pp. 223-232. DOI: https://doi.org/10.54216/JISIoT.160119
    Mahmoud, A. Shehab, A. S., A. Al-Ezaly, E. (2025). An Optimized Convolutional Neural Network for Alzheimer’s disease Detection. Journal of Intelligent Systems and Internet of Things, (), 223-232. DOI: https://doi.org/10.54216/JISIoT.160119
    Mahmoud, Amena. Shehab, Abdulaziz. S., A.. Al-Ezaly, Esraa. An Optimized Convolutional Neural Network for Alzheimer’s disease Detection. Journal of Intelligent Systems and Internet of Things , no. (2025): 223-232. DOI: https://doi.org/10.54216/JISIoT.160119
    Mahmoud, A. , Shehab, A. , S., A. , Al-Ezaly, E. (2025) . An Optimized Convolutional Neural Network for Alzheimer’s disease Detection. Journal of Intelligent Systems and Internet of Things , () , 223-232 . DOI: https://doi.org/10.54216/JISIoT.160119
    Mahmoud A. , Shehab A. , S. A. , Al-Ezaly E. [2025]. An Optimized Convolutional Neural Network for Alzheimer’s disease Detection. Journal of Intelligent Systems and Internet of Things. (): 223-232. DOI: https://doi.org/10.54216/JISIoT.160119
    Mahmoud, A. Shehab, A. S., A. Al-Ezaly, E. "An Optimized Convolutional Neural Network for Alzheimer’s disease Detection," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 223-232, 2025. DOI: https://doi.org/10.54216/JISIoT.160119