PMC:7782580 / 103-1163 JSON TXT

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Id	Subject	Object	Predicate	Lexical cue	tao:has_database_id
7	0-24	Disease	denotes	Coronavirus disease 2019	MESH:C000657245
8	26-34	Disease	denotes	COVID-19	MESH:C000657245
9	125-133	Disease	denotes	COVID-19	MESH:C000657245
10	242-250	Disease	denotes	COVID-19	MESH:C000657245
11	1030-1038	Disease	denotes	COVID-19	MESH:C000657245

Id	Subject	Object	Predicate	Lexical cue
T3	0-89	Sentence	denotes	Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks.
T4	90-188	Sentence	denotes	The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling.
T5	189-336	Sentence	denotes	We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity.
T6	337-671	Sentence	denotes	We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)).
T7	672-756	Sentence	denotes	Heatmaps are used to visualize the salient features extracted by the neural network.
T8	757-950	Sentence	denotes	The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework.
T9	951-1060	Sentence	denotes	The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.