Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3654 2019 2019 An Optimized Convolutional Neural Network for Alzheimer’s disease Detection Information Systems Department, Faculty of Computer and Information, Kafr Elsheikh University, 33511, Egypt Abdulaziz Abdulaziz 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 Department of Computer Science, Mansoura University, Mansoura 35516, Egypt; Department of Computer Science, Arab East Colleges, Riyadh 53354, Saudi Arabia A. S. Abohamama Information Systems Department, Faculty of Computer and Information, Mansoura University, Mansoura, 35516, Egypt Esraa Al Al-Ezaly 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. 2025 2025 223 232 10.54216/JISIoT.160119 https://www.americaspg.com/articleinfo/18/show/3654