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