id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-285510-qrivd52o	Zokaeinikoo, M.	AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images	2020-05-25		.txt	text/plain	4638	236	51	We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). This hierarchical structure enables the model to capture the dependency of features extracted from chest images via a pre-trained network (e.g., VGG-16) in both horizontal and vertical directions and helps improve model performance. While the early layers of VGG-16 learn low-level features of the image, our hierarchical attention model learns subtle signs of COVID-19 and other viral/bacterial infections and determines the final classification. The hierarchical attention model had a sensitivity (true positive rate) of 99.3%, a specificity (true negative rate) of 99.98%, and a positive predictive value (PPV) of 99.6% for detecting COVID-19 from chest radiography images (Figure 2) . These results suggest that AIDCOV performs well in detecting COVID-19, other viral/bacterial infections, and normal cases based on the chest radiography images.	./cache/cord-285510-qrivd52o.txt	./txt/cord-285510-qrivd52o.txt