{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007133832,0.001301073,0.0007999555,0.001567713,0.0003045131,0.00094787,0.001344724,0.001246584,0.001156883],"category_scores_gemma":[0.001491546,0.0004484036,0.0009700223,0.0006915014,0.0002776839,0.0008631525,0.001157079,0.001073531,0.0008302591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009417676,"about_ca_system_score_gemma":0.001312776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008848668,"about_ca_topic_score_gemma":0.01108818,"domain_scores_codex":[0.9994463,0.0000751428,0.00003622966,0.0001694595,0.0001291597,0.0001436403],"domain_scores_gemma":[0.9997163,0.00007045019,0.00004545527,0.00003316922,0.0001065157,0.00002802772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006887004,0.0006403987,0.01430555,0.0002877194,0.0002518951,0.0006312708,0.00009914887,0.2207696,0.03793441,0.002478716,0.02605316,0.6958594],"study_design_scores_gemma":[0.00002054509,0.00007592529,0.001570022,0.00002025853,0.00001945992,0.0001043862,0.00001883037,0.9893606,0.006294142,0.0009785406,0.001525124,0.0000122286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2520886,0.003413506,0.7239651,0.00117665,0.0003278136,0.0004474024,0.003345427,0.008836647,0.006398795],"genre_scores_gemma":[0.6859257,0.0009679666,0.2936478,0.0008235264,0.0001565951,0.0003355405,0.01017022,0.0001885888,0.007784112],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008848668,"threshold_uncertainty_score":0.01759434,"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."}}