id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-355218-eici4eit	Punn, Narinder Singh	Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks	2020-10-17		.txt	text/plain	5950	324	48	Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. [31] proposed a deep convolutional neural network based automatic prediction model of COVID-19 with the help of pre-trained transfer models using CXR images. Detection of coronavirus (covid-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest x-ray dataset	./cache/cord-355218-eici4eit.txt	./txt/cord-355218-eici4eit.txt