id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-350460-80eu9b9c	Che Azemin, Mohd Zulfaezal	COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings	2020-08-18		.txt	text/plain	2207	115	45	title: COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. With the recent opacity-related finding as an important characteristic in COVID-19 patients, this research is aimed at developing a deep learning model for the prediction of COVID-19 cases based on an existing pretrained model which was then retrained using adjudicated data set to recognize images with airspace opacity, an abnormality associated with COVID-19.	./cache/cord-350460-80eu9b9c.txt	./txt/cord-350460-80eu9b9c.txt