{"id":"W3043034578","doi":"10.1007/978-981-15-6634-9_4","title":"An Effective Vision Based Framework for the Identification of Tuberculosis in Chest X-Ray Images","year":2020,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Identification (biology); Computer science; Support vector machine; Tuberculosis; Artificial intelligence; Convolutional neural network; Artificial neural network; Machine learning; Random forest; Software; Pattern recognition (psychology); Medicine; Pathology","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":[0.0004878708,0.000587204,0.0007593525,0.001024478,0.0003314881,0.001228541,0.00125013,0.001158744,0.002824721],"category_scores_gemma":[0.0007300273,0.0003233653,0.001092162,0.0006649037,0.0005170679,0.0009072825,0.0008551812,0.0009737405,0.001099334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005819447,"about_ca_system_score_gemma":0.0007305796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006912809,"about_ca_topic_score_gemma":0.008263513,"domain_scores_codex":[0.9997073,0.00004287124,0.0000147759,0.00007168253,0.0001202011,0.00004310246],"domain_scores_gemma":[0.9998291,0.00004762914,0.00001308795,0.00002112176,0.00007316279,0.00001591629],"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":[0.0001264233,0.000158891,0.0004544165,0.0002254713,0.00008908209,0.0003393223,0.0001139991,0.09068929,0.06861889,0.03423383,0.01200048,0.7929499],"study_design_scores_gemma":[0.000006807042,0.00008735037,0.0006330057,0.00002770251,0.00003461207,0.0004080056,0.00003282418,0.9708963,0.01082311,0.01028945,0.006741032,0.00001992407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003542023,0.0008774964,0.99244,0.0001483436,0.00008584789,0.00005044139,0.0000782764,0.0006762751,0.002101372],"genre_scores_gemma":[0.125443,0.001842879,0.8599793,0.0003143382,0.0001923678,0.0001130354,0.0004282442,0.0001358567,0.01155104],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006912809,"threshold_uncertainty_score":0.01374513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03502254041768277,"score_gpt":0.3669911098178975,"score_spread":0.3319685694002147,"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."}}