Detection of COVID-19 in X-Ray Images Using Constrained Multi-view Spectral Clustering

Abstract

Machine learning, and specifically classification algorithms, has been widely used for the diagnosis of COVID-19 cases. However, these methods require knowing the labels of the datasets, and use a single view of the dataset. Due to the widespread of the COVID-19 cases, and the presence of the huge amount of patient datasets without knowing their labels, we emphasize in this paper to study, for the first time, the diagnosis of COVID-19 cases in an unsupervised manner. Thus, we can benefit from the abundance of datasets with missing labels. Nowadays, multi-view clustering attracts many interests. Spectral clustering techniques have attracted more attention thanks to a well-developed and solid theoretical framework. One of the major drawbacks of spectral clustering approaches is that they only provide a nonlinear projection of the data, which requires an additional clustering step. Since this post-processing step depends on numerous factors such as the initialization procedure or outliers, this can affect the quality of the final clustering. This paper provides an improved version of a recent method called Multiview Spectral Clustering via integrating Nonnegative Embedding and Spectral Embedding. In addition to keeping the benefits of this method, our proposed model incorporates two types of constraints: (i) a consistent smoothness of the nonnegative embedding across all views, and (ii) an orthogonality constraint over the nonnegative embedding matrix columns. Its advantages are demonstrated using COVIDx datasets. Besides, we test it with other image datasets to prove the right choice of this method in this study.