Classification of Alzheimer’s disease using Neuroimaging Data by Convolution Neural Network

Abstract

Alzheimer’s disease (AD) is a long-term condition that causes brain areas such as memory, recognition, judgment, and speech to deteriorate. Classification of AD using neuroimaging data like MRI using artificial intelligence has become a focus of current research. Likewise, deep learning recent breakthrough in computer vision has accelerated similar research. However, fundamental shortcomings of such algorithms include their dependency on a wide range of training datasets and the need for rigorous optimization of neural network architecture. In this paper, the deep learning approach 2D-Convolutional Neural Network (CNN) has been employed to analyse architectural significance in boosting the diagnosis accuracy of different classes of images-mild, very mild, moderate, and non-demented concerning AD with parameter optimization on neuroimaging dataset. Finally, the classification accuracy using the 2D-CNN architecture considering the impact of parameters such as dense units, dropout rate, and optimizers has led to 6.83% of Relative Improvement (RI) in contrast to the base model being developed.