Publication

Abstract : Purpose The study aims to develop an unsupervised framework using COVGANs to learn better visual representations of COVID-19 from unlabeled X-ray and CT scans. Methods We trained multiple-layer GANs to develop the COV-GAN framework on unlabeled X-ray and CT scans. We evaluated the quality of the learned representations using t-SNE visualization, K-means, and GMM clustering. The proposed unsupervised method’s performance was compared with leading unsupervised methods for COVID-19 classification on X-ray and CT scans. Results Our method achieved an accuracy of 75.1% on X-ray scans and 75.7% on CT scans, which is at least 13.9% and 12.3% higher than the leading unsupervised methods for COVID-19 classification on X-ray and CT scans, respectively. The t-SNE visualization, K-means, and GMM clustering showed that our method learned better visual representations of COVID-19 from unlabeled data. Conclusions Our unsupervised framework using COV-GANs can learn better visual representations of COVID-19 from unlabeled X-ray and CT scans. The learned representations can improve the performance of COVID-19 classification. The outcomes show the potential of unsupervised learning methods to overcome the dearth of labelled data in the medical profession, particularly in times of public health crises like the COVID-19 epidemic