id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-325235-uupiv7wh	Makris, A.	COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks	2020-05-24		.txt	text/plain	5435	304	50	In this research work the effectiveness of several state-of-the-art pre-trained convolutional neural networks was evaluated regarding the automatic detection of COVID-19 disease from chest X-Ray images. A collection of 336 X-Ray scans in total from patients with COVID-19 disease, bacterial pneumonia and normal incidents is processed and utilized to train and test the CNNs. Due to the limited available data related to COVID-19, the transfer learning strategy is employed. The proposed CNN is based on pre-trained transfer models (ResNet50, InceptionV3 and Inception-ResNetV2), in order to obtain high prediction accuracy from a small sample of X-ray images. Abbas et al [22] presented a novel CNN architecture based on transfer learning and class decomposition in order to improve the performance of pre-trained models on the classification of X-ray images. 22.20110817 doi: medRxiv preprint In this research work the effectiveness of several state-of-the-art pre-trained convolutional neural networks was evaluated regarding the detection of COVID-19 disease from chest X-Ray images.	./cache/cord-325235-uupiv7wh.txt	./txt/cord-325235-uupiv7wh.txt