An Optimized Convolutional Neural Network for Alzheimer’s disease Detection
Amena Mahmoud1, Abdulaziz Shehab2, 3,*, A. S. Abohamama4, 5, Esraa Al-Ezaly3
1Information Systems Department, Faculty of Computer and Information, Kafr Elsheikh University, 33511, Egypt
2Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sa-kaka 72388, Saudi Arabia
3Information Systems Department, Faculty of Computer and Information, Mansoura University, Mansoura, 35516, Egypt
4Department of Computer Science, Mansoura University, Mansoura 35516, Egypt
5Department of Computer Science, Arab East Colleges, Riyadh 53354, Saudi Arabia
Emails: amena_mahmoud@fci.kfs.edu.eg; abdulaziz_shehab@mans.edu.eg; abohamama@mans.edu.eg; esraagamal@mans.edu.eg
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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. |
Received: October 17, 2024 Revised: January 03, 2025 Accepted: February 05, 2025
Keywords: Alzheimer's; Magnetic resonance; Alzheimer’s disease detection; Brain disease