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arXiv · 2009.09899

Clustering COVID-19 Lung Scans

Jacob Householder·Andrew Householder·John Paul Gomez-Reed·Fredrick Park·Shuai Zhang
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Abstract

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.

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Jacob Householder, Andrew Householder, John Paul Gomez-Reed, Fredrick Park, Shuai Zhang. 2020-09-05. Clustering COVID-19 Lung Scans. https://arxiv.org/abs/2009.09899

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