{"id":"W4205163905","doi":"10.1109/smc52423.2021.9659019","title":"Efficient Deep Neural Network for an Automated Detection of COVID-19 using CT images","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Coronavirus disease 2019 (COVID-19); Radiography; Pneumonia; Artificial intelligence; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Radiology; Pandemic; Computed tomography; Population; Computer science; Medicine; Disease; Pathology; Internal medicine; Infectious disease (medical specialty)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000393541,0.0002339871,0.0004305843,0.0001728348,0.000126412,0.0001618868,0.000148305,0.0000968462,0.00008000954],"category_scores_gemma":[0.0003537416,0.0002364632,0.0001071395,0.0001987247,0.000105336,0.00005060659,0.00004997404,0.0001579698,0.000005890144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002815141,"about_ca_system_score_gemma":0.0003732275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008669487,"about_ca_topic_score_gemma":0.0002733844,"domain_scores_codex":[0.9979637,0.0001662846,0.000561636,0.0005220248,0.0005162666,0.0002701383],"domain_scores_gemma":[0.997932,0.0003480712,0.0003054309,0.0003113318,0.0008704197,0.0002327065],"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":[0.001282965,0.001539577,0.008065719,0.002535547,0.0009633376,0.000513634,0.001384775,0.7260098,0.2293861,0.01346994,0.005594222,0.009254394],"study_design_scores_gemma":[0.001453485,0.0003375207,0.001531725,0.0004422268,0.0001592178,0.0002393865,0.0005120774,0.9824704,0.007079407,0.00009616013,0.005446203,0.0002321977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97287,0.0006505029,0.01947314,0.002484246,0.002827901,0.0008361171,0.0001482277,0.0001670367,0.0005428246],"genre_scores_gemma":[0.9967707,0.00008623034,0.0006236322,0.001444385,0.0005250757,0.00004737834,0.0000983881,0.00003382315,0.0003703756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2564606,"threshold_uncertainty_score":0.9642689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08831818685297786,"score_gpt":0.3773266215262757,"score_spread":0.2890084346732978,"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."}}