Alzheimer’s Disease Classification Based on One Dimensional Convolutional Neural Network

This paper aims to solve the difficulties of Alzheimer's disease (AD) diagnosis by constructing a classification framework based on a one-dimensional convolutional neural network (CNN), which provides finer clinical cues and additional performance gain for AD diagnosis. For the selected MRI image data, it is divided into three types according to the degree of disease for further discussion, Mild Cognitive Impairment (MCI), and Normal Control (NC) Classification of the population. The framework first uses deep learning network to perform feature extraction on all pre-processed MRI slices, and then classifies them through 1-D CNN to achieve automatic classification of AD. A large amount of experimental data shows that the classification accuracy of the 1-D CNN framework for AD vs. MCI vs. NC, AD vs. MCI, AD vs. NC, MCI vs. NC has reached the average accuracy of 0.9550, 0.9933, 0.9945 and 0.9597, respectively, which show the proposed framework is fast and simple. It effectively improves the classification results over all the categories and achieves a superior classification performance for AD diagnosis.