id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-321852-e7369brf	Wang, Bo	AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system	2020-11-10		.txt	text/plain	6468	373	50	In this paper, we introduce a automatically AI system that can provide the probability of infection and the ranked IDs. Specifically, the proposed system which consists of classification and segmentation will save about 30-40% of the detection time for physicians and promote the performance of COVID-19 detection. Using the dataset, we train and evaluate several deep learning based models to detect and segment the COVID-19 regions. [34] also build a U-Net based segmentation model to separate lung lesions and extract the radiologic characteristics in order to predict the hospital stay of a patient. [42] develop three widelyused models, i.e., ResNet-50 [43] , Inception-V3 [44] , and Inception-ResNet-V2 [45] , to detect COVID-19 lesion in X-ray images and among them ResNet-50 achieves the best classification performance. The positive data for the segmentation models were those images with arbitrary lung lesion regions, regardless of whether the lesions were COVID-19 or not.	./cache/cord-321852-e7369brf.txt	./txt/cord-321852-e7369brf.txt