PMC:7782580 / 175-629 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"9","span":{"begin":53,"end":61},"obj":"Disease"},{"id":"10","span":{"begin":170,"end":178},"obj":"Disease"}],"attributes":[{"id":"A9","pred":"tao:has_database_id","subj":"9","obj":"MESH:C000657245"},{"id":"A10","pred":"tao:has_database_id","subj":"10","obj":"MESH:C000657245"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"ant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. 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 "}

{"project":"LitCovid-sentences","denotations":[{"id":"T4","span":{"begin":18,"end":116},"obj":"Sentence"},{"id":"T5","span":{"begin":117,"end":264},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"ant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. 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 "}