Enhancing Early Diagnosis of Alzheimer’s Disease in MRI Images using Deep CNN Model

Alzheimer’s disease (AD), a progressive brain disorder affecting individuals over 65, is characterized by memory loss and brain shrinkage. The early detection, crucial for preserving cognitive functions, often employs magnetic resonance imaging (MRI) techniques. This study has proposed and developed a deep convolutional neural network (CNN)-based model to classify Alzheimer’s stages, utilizing a publicly available comprehensive Alzheimer’s MRI dataset with images categorized into four classes: Non-Dementia (ND), Very Mild Dementia (VMD), Mild Dementia (MD), and Moderate Dementia (MOD). The proposed model architecture, containing layers for feature extraction and classification, achieved 99% accuracy, outperforming existing studies and highlighting its potential for improving early diagnosis and treatment strategies of Alzheimer’s disease.