id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-135296-qv7pacau	Polsinelli, Matteo	A Light CNN for detecting COVID-19 from CT scans of the chest	2020-04-24		.txt	text/plain	3833	194	56	We propose a light CNN design based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with other CT images (community-acquired pneumonia and/or healthy images). On the tested datasets, the proposed modified SqueezeNet CNN achieved 83.00% of accuracy, 85.00% of sensitivity, 81.00% of specificity, 81.73% of precision and 0.8333 of F1Score in a very efficient way (7.81 seconds medium-end laptot without GPU acceleration). In the present work, we aim at obtaining acceptable performances for an automatic method in recognizing COVID-19 CT images of lungs while, at the same time, dealing with reduced datasets for training and validation and reducing the computational overhead imposed by more complex automatic systems. In this work we developed, trained and tested a light CNN (based on the SqueezeNet) to discriminate between COVID-19 and community-acquired pneumonia and/or healthy CT images.	./cache/cord-135296-qv7pacau.txt	./txt/cord-135296-qv7pacau.txt