Alzheimer’s disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer’s disease
can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been
exploited by researchers for Alzheimer’s disease diagnosis. Analysing magnetic resonance imaging (MRI) is a common practice
for Alzheimer’s disease diagnosis in clinical research. Detection of Alzheimer’s disease is exacting due to the similarity in
Alzheimer’s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques
have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a
deep convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. While most of the existing
approaches perform binary classification, our model can identify different stages of Alzheimer’s disease and obtains superior
performance for early-stage diagnosis
