With the ongoing COVID-19 pandemic, understanding the characteristics of the virus has become an important and challenging task in the scientific community. While tests do exist for COVID-19, the goal of our research is to explore other methods of identifying infected individuals. Our group applied unsupervised clustering techniques to explore a dataset of lungscans of COVID-19 infected, Viral Pneumonia infected, and healthy individuals. This is an important area to explore as COVID-19 is a novel disease that is currently being studied in detail. Our methodology explores the potential that unsupervised clustering algorithms have to reveal important hidden differences between COVID-19 and other respiratory illnesses. Our experiments use: Principal Component Analysis (PCA), K-Means++ (KM++) and the recently developed Robust Continuous Clustering algorithm (RCC). We evaluate the performance of KM++ and RCC in clustering COVID-19 lung scans using the Adjusted Mutual Information (AMI) score.
@article{arxiv.2009.09899,
title = {Clustering COVID-19 Lung Scans},
author = {Jacob Householder and Andrew Householder and John Paul Gomez-Reed and Fredrick Park and Shuai Zhang},
journal= {arXiv preprint arXiv:2009.09899},
year = {2021}
}