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Alzheimer's disease (AD) is a progressive and incurable neurodegenerativedisease which destroys brain cells and causes loss to patient's memory. Anearly detection can prevent the patient from further damage of the brain cellsand hence avoid permanent memory loss. In past few years, various automatictools and techniques have been proposed for diagnosis of AD. Several methodsfocus on fast, accurate and early detection of the disease to minimize the lossto patients mental health. Although machine learning and deep learningtechniques have significantly improved medical imaging systems for AD byproviding diagnostic performance close to human level. But the main problemfaced during multi-class classification is the presence of highly correlatedfeatures in the brain structure. In this paper, we have proposed a smart andaccurate way of diagnosing AD based on a two-dimensional deep convolutionalneural network (2D-DCNN) using imbalanced three-dimensional MRI dataset.Experimental results on Alzheimer Disease Neuroimaging Initiative magneticresonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model issuperior in terms of accuracy, efficiency, and robustness. The model classifiesMRI into three categories: AD, mild cognitive impairment, and normal control:and has achieved 99.89% classification accuracy with imbalanced classes. Theproposed model exhibits noticeable improvement in accuracy as compared to thestate-fo-the-art methods.

Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data