{"id":"W4287812901","doi":"10.48550/arxiv.2004.10641","title":"Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT\\n Images: A Machine Learning-Based Approach","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Feature extraction; Machine learning; Deep learning; Feature (linguistics); Coronavirus disease 2019 (COVID-19); Classifier (UML); Pneumonia; Acute respiratory distress; Pattern recognition (psychology); Medicine; Disease; Infectious disease (medical specialty); Pathology; Lung","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001275392,0.001059618,0.0008887914,0.002721231,0.0002971228,0.0009208561,0.001087462,0.001338329,0.0009784466],"category_scores_gemma":[0.001581091,0.0002711058,0.0008832306,0.0009004162,0.0003183308,0.0006811153,0.0007181981,0.0007920057,0.0005970926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005197468,"about_ca_system_score_gemma":0.0007904299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0048161,"about_ca_topic_score_gemma":0.005946994,"domain_scores_codex":[0.9994015,0.0001001237,0.00005749536,0.0001924823,0.0001167002,0.0001316583],"domain_scores_gemma":[0.9994959,0.0001890587,0.00006603025,0.0000453145,0.0001514795,0.00005229873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001137036,0.000775955,0.01546271,0.000291586,0.0002063971,0.0003568589,0.000109256,0.04827641,0.06153723,0.001048789,0.005538706,0.8652592],"study_design_scores_gemma":[0.00003555817,0.0003358411,0.01227198,0.00005335751,0.00007222308,0.0004802483,0.0001043141,0.9614251,0.0216688,0.001259267,0.002262383,0.00003085829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3951144,0.006911017,0.5819359,0.0008606078,0.0003790669,0.0007556524,0.001997179,0.007087395,0.004958769],"genre_scores_gemma":[0.6996823,0.001475791,0.2914762,0.0003537459,0.0001497751,0.0002668774,0.003317054,0.0001188759,0.003159315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0048161,"threshold_uncertainty_score":0.009576082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1314330443294387,"score_gpt":0.2652888406167102,"score_spread":0.1338557962872715,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}