{"id":"W3118976902","doi":"10.18280/ts.370611","title":"Glioma Segmentation and Classification System Based on Proposed Texture Features Extraction Method and Hybrid Ensemble Learning","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Random forest; Artificial intelligence; Computer science; Pattern recognition (psychology); Local binary patterns; Thresholding; Segmentation; Feature extraction; Discrete wavelet transform; Wavelet; Fluid-attenuated inversion recovery; Wavelet transform; Histogram; Magnetic resonance imaging","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000300562,0.0001668192,0.0001334001,0.00010962,0.0003352064,0.0001671144,0.00006331741,0.00005593,0.00004521663],"category_scores_gemma":[0.0001110771,0.000153987,0.00003365536,0.0002272554,0.00004562443,0.0002115858,0.00001099732,0.0002434823,0.00001561143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007779978,"about_ca_system_score_gemma":0.00002351574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002870601,"about_ca_topic_score_gemma":9.3947e-7,"domain_scores_codex":[0.998278,0.0004432425,0.000237222,0.0005324862,0.0003553663,0.0001536755],"domain_scores_gemma":[0.9993335,0.0002137652,0.0002033711,0.0000853574,0.0000367928,0.0001272413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002530531,0.00004590274,0.00008388016,0.00009897527,0.000003760847,0.000008268066,0.0002834272,0.0006550332,0.9491919,0.001812279,0.0001617299,0.04740177],"study_design_scores_gemma":[0.0009257444,0.0003989353,0.007453242,0.0000318223,0.00002602576,0.00007049269,0.0006829411,0.3337281,0.655748,0.00002617902,0.00075204,0.0001564851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5160974,0.00003393382,0.4731719,0.005827585,0.0001747674,0.001293472,0.00001566828,0.0005522642,0.002833071],"genre_scores_gemma":[0.9980134,0.000004634883,0.000954767,0.0007684715,0.0001104744,0.0000510704,0.00001817263,0.00002025903,0.0000587449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.481916,"threshold_uncertainty_score":0.627941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03971627035062088,"score_gpt":0.2860447667298425,"score_spread":0.2463284963792216,"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."}}