{"id":"W3091282141","doi":"10.1109/access.2020.3025010","title":"DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thunder Bay Regional Research Institute; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Chest radiograph; Convolutional neural network; Coronavirus disease 2019 (COVID-19); Radiography; Artificial intelligence; Computer science; Deep learning; Pneumonia; Medicine; Radiology; Pattern recognition (psychology); Pathology; Disease; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003421403,0.0002523641,0.0003880902,0.0002842927,0.0002888062,0.0001527194,0.0003570619,0.0001995493,0.0000442429],"category_scores_gemma":[0.001648381,0.0002546857,0.0002532432,0.00119641,0.000116632,0.0002110542,0.00003233924,0.0003667187,0.00001772793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002962289,"about_ca_system_score_gemma":0.0003811848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001439664,"about_ca_topic_score_gemma":0.00003319235,"domain_scores_codex":[0.9981327,0.00006425948,0.000372114,0.0007221473,0.0003674992,0.0003412948],"domain_scores_gemma":[0.998066,0.000570939,0.0002329056,0.000393887,0.0002000686,0.0005362074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01010047,0.007071792,0.1526466,0.01968802,0.001390263,0.000107268,0.007152328,0.2695399,0.3157692,0.0004051536,0.0834965,0.1326325],"study_design_scores_gemma":[0.008035302,0.0006711173,0.03136737,0.00006426098,0.0004658274,0.00002346422,0.0001878333,0.52484,0.01985076,0.00002792598,0.4139426,0.000523549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02245635,0.000148264,0.9157695,0.05908207,0.000297842,0.001530459,0.00001469606,0.0005901992,0.0001105833],"genre_scores_gemma":[0.9356246,0.0000195428,0.00292333,0.05982741,0.0007273004,0.0006713909,0.0001005782,0.00007012868,0.00003571475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9131683,"threshold_uncertainty_score":0.9999905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.155369235923019,"score_gpt":0.3796503785839573,"score_spread":0.2242811426609383,"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."}}