  Alzheimer's disease is one of the diseases that mostly affects older people
without being a part of aging. The most common symptoms include problems with
communicating and abstract thinking, as well as disorientation. It is important
to detect Alzheimer's disease in early stages so that cognitive functioning
would be improved by medication and training. In this paper, we propose two
attention model networks for detecting Alzheimer's disease from MRI images to
help early detection efforts at the preclinical stage. We also compare the
performance of these two attention network models with a baseline model.
Recently available OASIS-3 Longitudinal Neuroimaging, Clinical, and Cognitive
Dataset is used to train, evaluate and compare our models. The novelty of this
research resides in the fact that we aim to detect Alzheimer's disease when all
the parameters, physical assessments, and clinical data state that the patient
is healthy and showing no symptoms
