{"id":"W4324291216","doi":"10.1007/978-981-19-9307-7_9","title":"kNN-SVM with Deep Features for COVID-19 Pneumonia Detection from Chest X-ray","year":2022,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Support vector machine; Artificial intelligence; Margin (machine learning); Computer science; Pattern recognition (psychology); Coronavirus disease 2019 (COVID-19); k-nearest neighbors algorithm; Machine learning; Algorithm; 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":[0.0007611673,0.0009636451,0.0008360608,0.0005967174,0.0002761639,0.0008095395,0.001208176,0.001112715,0.004558004],"category_scores_gemma":[0.001424847,0.000370645,0.0008457965,0.0006798886,0.0001575306,0.0009201948,0.001112537,0.001429532,0.003608267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004619093,"about_ca_system_score_gemma":0.0006758663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005652206,"about_ca_topic_score_gemma":0.008551908,"domain_scores_codex":[0.9996178,0.00005376767,0.0000323918,0.0001120877,0.0001187304,0.00006525961],"domain_scores_gemma":[0.9996117,0.0001148406,0.00001816238,0.00005320016,0.0001745301,0.00002757573],"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":[0.0001239788,0.0001001803,0.001327872,0.0001157497,0.00007284023,0.00005292235,0.00002262173,0.03047987,0.005398103,0.001574489,0.03255618,0.9281752],"study_design_scores_gemma":[0.00001115,0.00008464518,0.001212478,0.0000434739,0.00004801175,0.0001089512,0.00002280055,0.97998,0.004776796,0.004986525,0.008706896,0.00001819046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03580683,0.009611688,0.9253795,0.001240964,0.001557953,0.0001877964,0.002898118,0.01045668,0.0128605],"genre_scores_gemma":[0.4463506,0.004275837,0.4836145,0.001584024,0.0008515299,0.0003040099,0.01167303,0.0007582803,0.05058816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005652206,"threshold_uncertainty_score":0.015248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873190804118729,"score_gpt":0.2960133045012687,"score_spread":0.2672813964600814,"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."}}