Alzheimer’s Disease Detection using Deep Learning Techniques

Abstract

Alzheimer’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.