AISSA, BrahimBENYOUB, Nacer2024-11-032024-11-032024https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/8848Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative disor- der. Being the most common cause of dementia, it affects millions of people around the world, making early detection and diagnosis a necessity. Deep learning can help detect the numerous patterns associated with this disease, aiding in its early diag- nosis. In this work, we employ a transfer learning approach to classify MRI images into Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) classes by leveraging VGG16 and VGG19 models pre-trained on Im- ageNet. The datasets used for training are down-sampled and up-sampled datasets sampled from the ADNI dataset to mitigate the class imbalance issue, resulting in four experiments. Our approach yielded high accuracy rates ranging from 98.14% to 99.59%, with VGG19 trained on down-sampled data achieving the highest per- formance among the four models.enAlzheimer’s Disease, Deep learning, Transfer learning, Dementia, Convolutional Neural Networks, VGG, MRI.La maladie d’Alzheimer, Apprentissage profond, Apprentissage par transfert, démence, réseaux neuronaux convolutifs, VGG, IRM.Thesis