id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-299099-e1ajjte6	Brunese, Luca	Machine learning for coronavirus covid-19 detection from chest x-rays	2020-12-31		.txt	text/plain	3100	153	53	The remaining of the paper proceeds as follow: Section 2 presents the proposed method from COVID-19 detection from x-rays, Section 3 describes the performance results in the evaluation of real-world chest X-rays and, in the last section, conclusion and future works are drawn. The feature set is obtained from each chest X-ray and, with the associated label, it represents the input for the supervised machine learning algorithm, that will output the model. As shows from Figure 2 , in this phase we obtain the numerical features from a set of chest X-ray not considered in the previous phase: this represents the input for the model that will generate the prediction i.e., whether the input chest X-ray is related to the COVID-19 or to the other category. The effectiveness of the proposed feature set in discriminating between COVID-19 and other disease is organised in descriptive statistics i.e., boxplot analysis and the evaluation of the model obtained as output from the machine learning classifier.	./cache/cord-299099-e1ajjte6.txt	./txt/cord-299099-e1ajjte6.txt