{"id":"W3211206889","doi":"10.3390/diagnostics11111972","title":"COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Prince Mohammad Bin Fahd University","keywords":"Overfitting; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Deep learning; Transfer of learning; Computer science; Pneumonia; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Stage (stratigraphy); Reliability (semiconductor); Convolutional neural network; 2019-20 coronavirus outbreak; Process (computing); Machine learning; Pattern recognition (psychology); Medicine; Artificial neural network; Pathology; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003600976,0.000254903,0.0004447178,0.0002278997,0.0003164371,0.0001162814,0.00009542469,0.0002401593,0.0002056145],"category_scores_gemma":[0.02978654,0.0002822347,0.0001674286,0.0006207072,0.00009593183,0.000134893,0.0001368201,0.0005350234,0.00003227777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008731743,"about_ca_system_score_gemma":0.0008401717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002573468,"about_ca_topic_score_gemma":0.00006711418,"domain_scores_codex":[0.9981179,0.000190661,0.0003908043,0.000508632,0.0003868569,0.0004051673],"domain_scores_gemma":[0.9961418,0.002391916,0.0001576239,0.0004476664,0.0003428938,0.0005181023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006731095,0.003535001,0.1721893,0.003310424,0.0008928434,0.01622535,0.004504834,0.07379841,0.5466436,0.000640196,0.006708622,0.1708783],"study_design_scores_gemma":[0.003413748,0.0005241971,0.006577292,0.0007421003,0.001108858,0.001138493,0.0005913611,0.04150133,0.4498228,0.0003488754,0.493299,0.0009318935],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5041305,0.004344815,0.4522552,0.0329982,0.001379247,0.001603351,0.0000246225,0.002779688,0.0004843702],"genre_scores_gemma":[0.8443912,0.00471732,0.08941672,0.05972808,0.0008507514,0.000139942,0.0001337391,0.0001944942,0.0004277563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4865904,"threshold_uncertainty_score":0.999963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03678074274345097,"score_gpt":0.3461401176768029,"score_spread":0.3093593749333519,"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."}}