Clustering COVID-19 Lung Scans
This work addresses the challenge of COVID-19 diagnosis for medical professionals, but it is incremental as it applies existing methods to a new dataset.
The researchers tackled the problem of identifying COVID-19 infections by applying unsupervised clustering techniques to lung scans of COVID-19, viral pneumonia, and healthy individuals, achieving results evaluated with the Adjusted Mutual Information score.
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.