Design and Implementation of a Method for Diagnosing Various Stages of Alzheimer’s Illness Using Deep Learning

Abstract

Alzheimer’s disease (AD) is meant to be an irreversible degenerative brain disease and it is progressive. About 50 million individuals world-wide suffer dementia, with about 10 million new cases every year. There is currently no cure for condition. Dementia cannot be treated unless it is detected early. Therefore, early detection of Alzheimer’s requires a computational approach. Our project seeks to classify and determine deep leaning methods for the AD stage. Build a model mistreatment the ADNI knowledge set. The employee data is initial augmented to increase the scale of the dataset by adding additional data and increasing the variability of the dataset. MRI pictures are pre-processed with noise suppression.