PMC:7782580 / 42-722 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"7","span":{"begin":61,"end":85},"obj":"Disease"},{"id":"8","span":{"begin":87,"end":95},"obj":"Disease"},{"id":"9","span":{"begin":186,"end":194},"obj":"Disease"},{"id":"10","span":{"begin":303,"end":311},"obj":"Disease"}],"attributes":[{"id":"A7","pred":"tao:has_database_id","subj":"7","obj":"MESH:C000657245"},{"id":"A8","pred":"tao:has_database_id","subj":"8","obj":"MESH:C000657245"},{"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":"medical images using convolutional neural networks\n\nAbstract\nCoronavirus disease 2019 (COVID-19) is a global pandemic posing significant 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 experts and provides high scores for multiple statistical indices (F1 scores \u003e 96.72% (0.9307"}

{"project":"LitCovid-sentences","denotations":[{"id":"T2","span":{"begin":52,"end":60},"obj":"Sentence"},{"id":"T3","span":{"begin":61,"end":150},"obj":"Sentence"},{"id":"T4","span":{"begin":151,"end":249},"obj":"Sentence"},{"id":"T5","span":{"begin":250,"end":397},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"medical images using convolutional neural networks\n\nAbstract\nCoronavirus disease 2019 (COVID-19) is a global pandemic posing significant 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 experts and provides high scores for multiple statistical indices (F1 scores \u003e 96.72% (0.9307"}