id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-355441-0b266hwn	Misztal, Krzysztof	The importance of standardisation – COVID-19 CT&Radiograph Image Data Stock for deep learning purpose	2020-10-28		.txt	text/plain	3074	188	52	The aim of COVID-19 CT&Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. Training neural networks on these datasets requires including samples from additional data sources such as common bacterial pneumonia [11] or lung nodule analysis [12, 13] . Apostolopoulos and Mpesiana [14] used a MobileNet v2 [15] pre-trained on 55 ImageNet [16] for fine-tuning on two datasets which were created using samples from COVID-19 Image Data Collection [10] , COVID-19 X-ray collection available on kaggle [17] , and a dataset containing radiograph scans of common bacterial 3 J o u r n a l P r e -p r o o f pneumonia [11] . Therefore, following [14] , we decided to enrich the COVID-19-negative class with radiograph images from dataset of common bacterial pneumonia [11] .	./cache/cord-355441-0b266hwn.txt	./txt/cord-355441-0b266hwn.txt