id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-319868-rtt9i7wu	Majeed, Taban	Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays	2020-10-06		.txt	text/plain	7666	377	52	In recent months, much research came out addressing the problem of COVID-19 detection in chest X-rays using deep learning approaches in general, and convolutional neural networks (CNNs) in particular [3] [4] [5] [6] [7] [8] [9] [10] . [3] built a deep convolutional neural network (CNN) based on ResNet50, InceptionV3 and Inception-ResNetV2 models for the classification of COVID-19 Chest X-ray images to normal and COVID-19 classes. [9] , authors use CT images to predict COVID-19 cases where they deployed Inception transfer-learning model to establish an accuracy of 89.5% with specificity of 88.0% and sensitivity of 87.0%. Wang and Wong [2] investigated a dataset that they called COVIDx and a neural network architecture called COVID-Net designed for the detection of COVID-19 cases from an open source chest X-ray radiography images. The deep learning architectures that we used for the purpose of COVID19 detection from X-ray images are AlexNet, VGG16, VGG19, ResNet18, ResNet50, ResNet101, Goog-leNet, InceptionV3, SqueezeNet, Inception-ReseNet-v2, Xception and DenseNet201.	./cache/cord-319868-rtt9i7wu.txt	./txt/cord-319868-rtt9i7wu.txt