id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-314849-owqq0lev	Apostolopoulos, Ioannis D.	Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks	2020-04-03		.txt	text/plain	2944	157	50	The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. To achieve this, a collection of 1427 thoracic X-ray scans is processed and utilized to train and test the CNNs. Due to the fact that the size of the samples related to Covid-19 is small (224 images), transfer learning is a preferable strategy to train the deep CNNs. This is due to the fact that the state-of-the-art CNNs are sophisticated model requiring large-scale datasets to perform accurate feature extraction and classification. The results are encouraging and demonstrate the effectiveness of deep learning, and more specifically, transfer learning with CNNs to the automatic detection of abnormal X-ray images from small datasets, related to the Covid-19 disease.	./cache/cord-314849-owqq0lev.txt	./txt/cord-314849-owqq0lev.txt