论文标题
聚类covid-19肺扫描
Clustering COVID-19 Lung Scans
论文作者
论文摘要
随着持续的Covid-19大流行，了解该病毒的特征已成为科学界的一项重要且具有挑战性的任务。尽管Covid-19确实存在测试，但我们研究的目的是探索其他识别感染者的方法。我们的小组采用了无监督的聚类技术来探索被感染，病毒性肺炎感染和健康个体的COVID-19的肺部数据集。这是探索的重要领域，因为Covid-19是一种新型疾病，目前正在详细研究。我们的方法探讨了无监督的聚类算法必须揭示Covid-19与其他呼吸系统疾病之间重要的隐藏差异。我们的实验使用：主成分分析（PCA），K-Means ++（KM ++）和最近开发的可靠连续聚类算法（RCC）。我们使用调整后的共同信息（AMI）评分评估了km ++和RCC在聚类covid-19肺扫描中的性能。
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.
