A Semi-supervised Generative Adversarial Network Algorithm for Alzheimer's Disease Analysis

Abstract

Currently, the study of Alzheimer's disease (AD) imaging classification based on deep learning has become a research hotspot. But due to the characteristics of AD samples with lack of labels and small samples, there are some difficulties in classifying task. In this paper, Semi-supervised generative adversarial network algorithm is designed. Firstly, an improved generative network algorithm is designed to extract and inherit features related to AD, while ignoring non-disease related variations of AD to the disease to generate new samples, achieving sample size expansion and data enhancement. Then, an unsupervised clustering algorithm is constructed to generate sample clustering categories, so that the new samples have different types of AD brain atrophy labels .The test results show that the algorithm achieves good and stable clustering on the real sample test dataset (ADNI-1), and identifies four types of AD brain atrophy patterns. The Calinski-Harabasz Index of the algorithm is calculated about 2388, and the Silhouette Coefficient Index is calculated about 0.588. With these cluster indexes, the algorithm has better clustering performance than traditional clustering methods such as k-means. These research results will contribute to further studying the classification of AD, and contribute to the analysis and diagnosis of the etiology of AD.