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

Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. Early detection can prevent the patient from further damage to the brain cells and hence avoid permanent memory loss. In the past few years, various automatic tools and techniques have been proposed for the diagnosis of AD. Several methods focus on fast, accurate, and early detection of the disease to minimize the loss to a patient's mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for AD by providing diagnostic performance close to the 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 an imbalanced three-dimensional MRI dataset. Experimental results on Alzheimer's 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 state-of-the-art methods.