Early Diagnosis and Classification of Alzheimer's Disease Using Convolutional Neural Network Based on MRI Images

Alzheimer's disease (AD) is a progressive neurode-generative disorder that affects memory, thinking, and behavior, gradually leading to mild to severe dementia. Over the past decade, researchers have leveraged deep learning models, such as Convolutional Neural Networks (CNN) and transfer learning, for the early detection and classification of AD. In this paper, we propose a simple and computationally cost-effective CNN architecture for the early diagnosis and classification of AD. The model was trained using ADNI T1-weighted 2D MRI scans, encompassing cognitively normal (CN) individuals, stable mild cognitive impairment (sMCI) cases, and Alzheimer's dementia (AD) patients. Experimental results demonstrate the superiority of our model, achieving an accuracy of 98.97%, thereby surpassing most previous studies while utilizing a low-cost custom CNN architecture.