{"id":"W4321373455","doi":"10.3390/computers12020044","title":"A Performance Study of CNN Architectures for the Autonomous Detection of COVID-19 Symptoms Using Cough and Breathing","year":2023,"lang":"en","type":"article","venue":"Computers","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Residual neural network; Computer science; Deep learning; Recall; F1 score; Pattern recognition (psychology); Coronavirus disease 2019 (COVID-19); Artificial neural network; Machine learning; Medicine; Psychology; Cognitive psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00212688,0.001471636,0.0004710511,0.001179344,0.0002866105,0.0007371356,0.0006807668,0.0008826976,0.001199903],"category_scores_gemma":[0.004741081,0.0002785675,0.0006069379,0.0005795024,0.0003367324,0.001307838,0.0005098063,0.0007240988,0.0006293569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001250374,"about_ca_system_score_gemma":0.0006440314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01237869,"about_ca_topic_score_gemma":0.01473767,"domain_scores_codex":[0.9992965,0.0001449702,0.00006022101,0.0002191441,0.0001642992,0.0001147963],"domain_scores_gemma":[0.998486,0.0007660729,0.000127503,0.0001527982,0.0004050576,0.00006269357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001959492,0.0004517814,0.07363873,0.001011625,0.0008757287,0.0003549331,0.000184591,0.1953989,0.03817041,0.002393968,0.01497712,0.6705827],"study_design_scores_gemma":[0.00005580065,0.001203619,0.03334134,0.000191079,0.0002419142,0.0003068547,0.0001371603,0.919857,0.03864304,0.001089523,0.0048696,0.00006312967],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.907868,0.01113651,0.05999673,0.001182301,0.0004454305,0.0002397017,0.003182802,0.002609881,0.01333866],"genre_scores_gemma":[0.9667358,0.001322938,0.0233255,0.0002547475,0.00007825474,0.00008440836,0.004139208,0.0001001478,0.003959097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01237869,"threshold_uncertainty_score":0.02461326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04821534159939773,"score_gpt":0.3366034340494025,"score_spread":0.2883880924500048,"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."}}