id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-249065-6yt3uqyy	Kassani, Sara Hosseinzadeh	Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach	2020-04-22		.txt	text/plain	4285	229	45	To the best of our knowledge, this research is the first comprehensive study of the application of machine learning (ML) algorithms (15 deep CNN visual feature extractor and 6 ML classifier) for automatic diagnoses of COVID-19 from X-ray and CT images. • With extensive experiments, we show that the combination of a deep CNN with Bagging trees classifier achieves very good classification performance applied on COVID-19 data despite the limited number of image samples. Motivated by the success of deep learning models in computer vision, the focus of this research is to provide an extensive comprehensive study on the classification of COVID-19 pneumonia in chest X-ray and CT imaging using features extracted by the stateof-the-art deep CNN architectures and trained on machine learning algorithms. The experimental results on available chest X-ray and CT dataset demonstrate that the features extracted by DesnseNet121 architecture and trained by a Bagging tree classifier generates very accurate prediction of 99.00% in terms of classification accuracy.	./cache/cord-249065-6yt3uqyy.txt	./txt/cord-249065-6yt3uqyy.txt