{"id":"W3045874508","doi":"10.18280/ts.370313","title":"A Deep Learning Based Hybrid Approach for COVID-19 Disease Detections","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Deep learning; Architecture; Computer science; Artificial intelligence; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Layer (electronics); Infection rate; Disease; Pattern recognition (psychology); Medicine; Geography; Infectious disease (medical specialty); Materials science; Pathology; Nanotechnology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002803777,0.000217461,0.0002844944,0.0001267635,0.0003140205,0.00005017015,0.0001197398,0.00003886416,0.0006464622],"category_scores_gemma":[0.001100516,0.0002175167,0.0002557375,0.0002555141,0.00006851351,0.00006520169,0.0000319378,0.0002230804,0.00002140057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002747653,"about_ca_system_score_gemma":0.0005022593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002611393,"about_ca_topic_score_gemma":0.000002635756,"domain_scores_codex":[0.9983419,0.00008950513,0.0003130075,0.0005282034,0.0003778997,0.0003495419],"domain_scores_gemma":[0.9981127,0.0004353645,0.00009517984,0.000171736,0.00008328149,0.001101746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007191783,0.003329082,0.02338273,0.005851947,0.0005642021,0.000368013,0.002499612,0.8562447,0.01426418,0.0002882804,0.06181719,0.02419833],"study_design_scores_gemma":[0.005085095,0.0007244837,0.001978136,0.00003576973,0.0004495174,0.000004126775,0.0001310734,0.794196,0.0009610199,0.00002145341,0.196164,0.0002492626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01301866,0.0001734941,0.9233303,0.06135029,0.00004694768,0.001543829,0.00004400173,0.0004430135,0.00004948275],"genre_scores_gemma":[0.9010571,0.000005976613,0.0075247,0.090013,0.0004335419,0.0006174618,0.0002571396,0.00005073579,0.00004033059],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9158056,"threshold_uncertainty_score":0.8870072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05860539531632419,"score_gpt":0.3105181200786568,"score_spread":0.2519127247623326,"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."}}