{"id":"W4229378127","doi":"10.1002/ima.22742","title":"<scp>OVASO</scp>: Integrated binary <scp>CNN</scp> models to classify <scp>COVID</scp>‐19, pneumonia and healthy lung in X‐ray images","year":2022,"lang":"en","type":"article","venue":"International Journal of Imaging Systems and Technology","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Research Foundation of Korea","keywords":"Coronavirus disease 2019 (COVID-19); Initialization; Transfer of learning; Normalization (sociology); Computer science; Binary number; Binary classification; Artificial intelligence; Classifier (UML); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Machine learning; Pattern recognition (psychology); Medicine; Support vector machine; Mathematics; Internal medicine","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":[0.0003673086,0.001148743,0.0003761826,0.0005702698,0.0001956769,0.0005619106,0.001147815,0.0009054082,0.003575694],"category_scores_gemma":[0.0009500305,0.0002626387,0.0006673784,0.0003372446,0.0002516413,0.0007514803,0.0006257658,0.0007229177,0.0007357397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004997239,"about_ca_system_score_gemma":0.0007166349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02139254,"about_ca_topic_score_gemma":0.02075944,"domain_scores_codex":[0.9998568,0.00001756744,0.000007322984,0.0000456912,0.00003902023,0.0000336234],"domain_scores_gemma":[0.999781,0.0000451322,0.00002042171,0.00003276697,0.00009722633,0.00002356729],"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":[0.0006126738,0.0003080009,0.009669407,0.0001737078,0.0002729855,0.0004162316,0.00004960654,0.5942248,0.02764081,0.001940311,0.03360958,0.3310821],"study_design_scores_gemma":[0.000006488375,0.00005027242,0.0005968832,0.000005783481,0.00001080827,0.00002098732,0.000005063823,0.9956105,0.002947555,0.0002194339,0.0005211205,0.000005165501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6130096,0.002050918,0.3402195,0.002079968,0.0009429093,0.0003945052,0.004519161,0.02040493,0.01637851],"genre_scores_gemma":[0.9304531,0.0003062615,0.05460777,0.0006185899,0.00009013762,0.0001025871,0.004675039,0.0002543335,0.008892164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02139254,"threshold_uncertainty_score":0.04253602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899184845796228,"score_gpt":0.3081964850158889,"score_spread":0.2892046365579267,"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."}}