Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3722 2019 2019 Department of Basic Science, College of Dentistry, University of Baghdad, Iraq Othman Othman Uuniversity of information Technology and Communication, Biomedical Informatics College, Baghdad, Iraq Mohammed Q. Jawad Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Othman Mohammed Jasim 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. 2025 2025 202 213 10.54216/JISIoT.160215 https://www.americaspg.com/articleinfo/18/show/3722