{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008315032,0.001451724,0.0008840687,0.002328321,0.0004544647,0.0009891659,0.001478043,0.001398002,0.001956827],"category_scores_gemma":[0.0009358671,0.0004322945,0.001145505,0.0009766797,0.0003094418,0.001244527,0.001228534,0.001188312,0.001133764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008731821,"about_ca_system_score_gemma":0.000945625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008446781,"about_ca_topic_score_gemma":0.0137441,"domain_scores_codex":[0.9994823,0.00006264413,0.00003291506,0.0001849485,0.0001158256,0.0001214554],"domain_scores_gemma":[0.9996566,0.00007779283,0.00003354265,0.00003721916,0.0001523441,0.00004260375],"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":[0.0007289905,0.0006118195,0.02121872,0.0003050642,0.0005874243,0.0006397295,0.0001896363,0.1113759,0.03087528,0.002760047,0.01671819,0.8139892],"study_design_scores_gemma":[0.00002393993,0.0001990842,0.003225246,0.00003333411,0.0001129932,0.0002797525,0.00006193629,0.9820595,0.008537778,0.002310127,0.003128113,0.00002814844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1913973,0.005737638,0.7777112,0.001712698,0.0005711975,0.0003054174,0.002463288,0.00998756,0.01011366],"genre_scores_gemma":[0.7835096,0.001220399,0.1936657,0.001093808,0.0002419433,0.0001847732,0.004044007,0.0001646628,0.01587513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008446781,"threshold_uncertainty_score":0.01679522,"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."}}