id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-197480-qmzkpcmn	Motamed, Saman	RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray	2020-10-06		.txt	text/plain	4106	216	53	In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). Wang et al.'s CNN based COVID-NET [12] achieved a 93.3% test accuracy for multi-class classification on a test cohort of 100 Normal, 100 Pneumonia and 100 COVID-19 from the COVIDx dataset with the rest of images of each class being used to train their model. In this study, we introduced RANDGAN, a novel generative adversarial network for semi-supervised detection of an unknown (COVID-19) class in chest X-ray images from a pool of known (Normal and Pneumonia) and unknown classes (COVID-19) by only using the known classes for training. By using transfer learning and segmenting the lung, we showed that using lung only images boosts the performance of generative models in detecting COVID-19 from Pneumonia and Normal images.	./cache/cord-197480-qmzkpcmn.txt	./txt/cord-197480-qmzkpcmn.txt