id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-155804-ft2pbgsl	Yamac, Mehmet	Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images	2020-05-08		.txt	text/plain	6021	347	55	To address this deficiency, Convolution Support Estimation Network (CSEN) has recently been proposed as a bridge between model-based and Deep Learning approaches by providing a non-iterative real-time mapping from query sample to ideally sparse representation coefficient' support, which is critical information for class decision in representation based techniques. The socalled Convolutional Support Estimation Network (CSEN) uses a pre-defined dictionary and learns a direct mapping using moderate/low size training set, which maps query samples, y, directly to the support set of representation coefficients, x (as it should be purely sparse in the ideal case). Having the pre-trained CheXNet for feature extraction, we develop two different strategies to obtain the classes of query X-ray images: (i) using collaborative representation-based classification with a proper preprocessing; (ii) a slightly modified version of our recently proposed convolution support estimator (CSEN) models.	./cache/cord-155804-ft2pbgsl.txt	./txt/cord-155804-ft2pbgsl.txt