id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-326081-9gh6tj7g	Jaiswal, A. K.	COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans	2020-07-10		.txt	text/plain	4966	290	53	To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. [1] suggested a deep neural network-driven model for prediction of Covid-19 which is termed as Covid-Net trained on the dataset COVIDx. The architecture built on a PEPX design pattern was first pre-trained on ImageNet and also utilized data augmentation. In this paper, we consider a task of identifying COVID-19 disease which is a binary classification, where the input to COVIDPEN is a chest X-ray or CTs image I x and the model outputs a binary label P y ∈ {positive, negative} delineating whether the coronavirus prediction is positive or negative. In this work, a deep neural network-based classifier is proposed i.e., COVIDPEN to diagnose COVID-19 and non-COVID-19 cases from chest radiographs and chest X-ray datasets. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks	./cache/cord-326081-9gh6tj7g.txt	./txt/cord-326081-9gh6tj7g.txt