Chest Disease Image Classification Based on Spectral Clustering Algorithm

Abstract

Nowadays, the emergence of new technologies gives rise to a huge amount of data in different fields such as public transportation, community services, scientific research, etc. Due to the aging population, healthcare is becoming more important in our daily life to reduce public burdens. For example, manually archiving massive electronic medical files, such as X-ray images, is impossible. However, precise classification is essential for further work, such as diagnosis. In this report, we applied a spectral clustering algorithm to classify chest disease X-ray images. We also employed the "pure" K-means algorithm for comparison. Three types of indexes are used to quantify the performances of both algorithms. Our analysis result shows that spectral clustering can successfully classify chest X-ray images based on the presence of disease spots on the lungs and the performance is superior to “pure" K-means clustering.