Alzheimer’s Disease Detection: A Deep Learning-Based Approach

Abstract

Mental health is an important part of a successful life for a person whether elderly, children, or young. Alzheimer’s is a fatal brain disease that severely damages the human brain, especially in the elderly. One way to prevent Alzheimer's disease is by detecting it early. The proposed research employs a deep learning methodology using a 3D convolutional neural network (3D CNN) that has been proposed to detect Alzheimer's disease at an early stage. The proposed model is primarily evaluated using three-dimensional brain images. A series of preprocessing have been applied that is an advanced normalization tool (ANT). The underlying pattern has a size of 128×128×64 and is passed to 17 layers of a neural network that is 3D-CNN. Another contribution of this study is the conversion of a 3D Alzheimer’s image into a 2D image. A 2D convolutional neural network such that RestNET50 and VGG16 are proposed to be used for Alzheimer’s detection. The proposed model has attained the highest of 78.07% accuracy using 3D CNN.