  Alzheimer's disease is a progressive neurodegenerative disorder that
primarily affects cognitive functions such as memory, thinking, and behavior.
In this disease, there is a critical phase, mild cognitive impairment, that is
really important to be diagnosed early since some patients with progressive MCI
will develop the disease. This study delves into the challenging task of
classifying Alzheimer's disease into four distinct groups: control normal (CN),
progressive mild cognitive impairment (pMCI), stable mild cognitive impairment
(sMCI), and Alzheimer's disease (AD). This classification is based on a
thorough examination of PET scan images obtained from the ADNI dataset, which
provides a thorough understanding of the disease's progression. Several
deep-learning and traditional machine-learning models have been used to detect
Alzheimer's disease. In this paper, three deep-learning models, namely VGG16
and AlexNet, and a custom Convolutional neural network (CNN) with 8-fold
cross-validation have been used for classification. Finally, an ensemble
technique is used to improve the overall result of these models. The results
show that using deep-learning models to tell the difference between MCI
patients gives an overall average accuracy of 93.13% and an AUC of 94.4%.
