Alzheimer’s Disease Diagnosis Using Parallel Convolutional Neural Networks

Abstract

Alzheimer's disease is progressive brain cell damage. This damage can be slowed down if the disease is detected at an early stage. Computer-Aided detection systems bring great help for this problem especially with the development of convolution neural networks. This work proposes an Alzheimer's disease diagnostic approach based on a segmentation technique and convolution neural networks. We segment an MRI brain image by a recursive thresholding process. This segmentation generates a set of output images witch simulate progressive brain degradation. Convolution neural networks capture this degradation and assign a value that indicates Alzheimer's disease stage.