id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-253343-3dmuxts5	Zhang, Ruochi	COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images	2020-09-21		.txt	text/plain	4078	247	56	title: COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images A two-step transfer learning model (COVID19XrayNet) was proposed to provide a candidate solution for training an accurate neural network model using the existing small dataset of COVID-19 X-ray images. Firstly, a pre-trained deep residual network (DRN) model ResNet34 was fine-tuned on a large dataset of pneumonia chest X-ray images. The pre-trained model ResNet34 may be used to detect COVID-19 patients based on the chest X-ray images through fine-tuning on a small dataset of COVID-19 images. This study transferred the pre-trained ResNet34 model to the COVID-19 detection problem based on the chest X-ray images. In the first step of our pipeline, the pre-trained model ResNet34 was transferred to the dataset dsPneumonia and the proposed framework COVID19XrayNet(2) was utilized to tune the parameters of the internal layers.	./cache/cord-253343-3dmuxts5.txt	./txt/cord-253343-3dmuxts5.txt