  Alzheimer's disease (AD) is a progressive and incurable neurodegenerative
disease which destroys brain cells and causes loss to patient's memory. An
early detection can prevent the patient from further damage of the brain cells
and hence avoid permanent memory loss. In past few years, various automatic
tools and techniques have been proposed for diagnosis of AD. Several methods
focus on fast, accurate and early detection of the disease to minimize the loss
to patients mental health. Although machine learning and deep learning
techniques have significantly improved medical imaging systems for AD by
providing diagnostic performance close to human level. But the main problem
faced during multi-class classification is the presence of highly correlated
features in the brain structure. In this paper, we have proposed a smart and
accurate way of diagnosing AD based on a two-dimensional deep convolutional
neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset.
Experimental results on Alzheimer Disease Neuroimaging Initiative magnetic
resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is
superior in terms of accuracy, efficiency, and robustness. The model classifies
MRI into three categories: AD, mild cognitive impairment, and normal control:
and has achieved 99.89% classification accuracy with imbalanced classes. The
proposed model exhibits noticeable improvement in accuracy as compared to the
state-fo-the-art methods.
