id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-288030-69e8cmy2	Ardakani, Ali Abbasian	Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks	2020-04-30		.txt	text/plain	3414	211	51	title: Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks indicated that a deep convolutional neural network (CNN) could detect lung nodules with a competition performance metric of 0.7967. In this study, we propose a CAD system based on deep learning to classify COVID-19 infection versus other atypical and viral pneumonia diseases. During the COVID-19 pandemic, the entirety of patients representing flu-like symptoms with an initial diagnosis of the novel coronavirus, regardless of demographic values such as age and sex, were included in the study. In this study, ten well-known pre-trained CNN were used to distinguish infection of COVID-19 from non-COVID-19 group: 1-AlexNet, 2-VGG-16, 3-VGG-19, 4-SqueezeNet, 5-GoogleNet, 6-MobileNet-V2, 7-ResNet-18, 8-ResNet-50, 9-ResNet-101, and 10-Xception ( Fig. 3 ). In conclusion, a CAD approach based on CT images with promising potential was proposed to distinguish infection of COVID-19 from other atypical and viral pneumonia diseases.	./cache/cord-288030-69e8cmy2.txt	./txt/cord-288030-69e8cmy2.txt