id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-296359-pt86juvr	Polsinelli, Matteo	A Light CNN for detecting COVID-19 from CT scans of the chest	2020-10-03		.txt	text/plain	3887	201	54	In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. Also the average classification time on a high-end workstation, 1.25 seconds, is very competitive with respect to that of more complex CNN designs, 13.41 seconds, witch require pre-processing. We started from the model of the SqueezeNet CNN to discriminate between COVID-19 and community-acquired pneumonia and/or healthy CT images. In this arrangement the number of images from the italian dataset used to train, validate and Test-1 are 60, 20 and 20, respectively. For each dataset arrangement we organized 4 experiments in which we tested different CNN models, transfer learning and the effectiveness of data augmentation. For each attempt, the CNN model has been trained for 20 epochs and evaluated by the accuracy results calculated on the validation dataset.	./cache/cord-296359-pt86juvr.txt	./txt/cord-296359-pt86juvr.txt