{"id":"W3165071810","doi":"10.1016/j.imu.2021.100620","title":"COV-SNET: A deep learning model for X-ray-based COVID-19 classification","year":2021,"lang":"en","type":"article","venue":"Informatics in Medicine Unlocked","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deep learning; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Transfer of learning; Computer science; Robustness (evolution); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Machine learning; Offset (computer science); Artificial neural network; Medicine; Infectious disease (medical specialty); Pathology; Disease","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001107554,0.001340534,0.0007984971,0.001390525,0.0004788545,0.001043282,0.002214737,0.001752217,0.002850361],"category_scores_gemma":[0.002735378,0.0004242878,0.001159715,0.001018983,0.0004703897,0.001231532,0.00120977,0.002126217,0.001103689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295701,"about_ca_system_score_gemma":0.001595714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01267996,"about_ca_topic_score_gemma":0.01762534,"domain_scores_codex":[0.999633,0.00007211135,0.0000316389,0.0001117999,0.00008750038,0.0000639127],"domain_scores_gemma":[0.9993382,0.0002494109,0.00007147514,0.00006483806,0.0002181415,0.00005790878],"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":[0.0009782295,0.0007936856,0.03198959,0.0003313001,0.0004358946,0.0005062395,0.0001182202,0.5092828,0.005791036,0.006799557,0.05023851,0.3927349],"study_design_scores_gemma":[0.00001367046,0.00006415031,0.0008488845,0.00002339145,0.00001780082,0.00006770631,0.00001229646,0.993593,0.00146401,0.002173362,0.001711273,0.00001054055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1746235,0.003818372,0.7877123,0.003233667,0.0008221734,0.0004824144,0.009687473,0.01038997,0.009230193],"genre_scores_gemma":[0.7685587,0.00164938,0.1940684,0.00150673,0.0002933234,0.0004466309,0.01977732,0.000306794,0.01339256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01267996,"threshold_uncertainty_score":0.02521229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09894683164934899,"score_gpt":0.388953911736358,"score_spread":0.290007080087009,"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."}}