{"id":"W4402308503","doi":"10.18280/ts.410426","title":"Performance Evaluation of Feature Extraction and SVM for Brain Tumor Detection Using MRI Images","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Computer science; Brain tumor; Feature (linguistics); Extraction (chemistry); Chromatography; Medicine; Chemistry; Pathology","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":[0.0009191833,0.0001055094,0.0000879378,0.000155523,0.0001687265,0.00008007066,0.00004938401,0.00004077844,0.00008252712],"category_scores_gemma":[0.0001163684,0.0001001594,0.00004861037,0.0002412331,0.00005792634,0.0004162234,0.00000835731,0.0001038049,0.000003309699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054784,"about_ca_system_score_gemma":0.00005037251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003528417,"about_ca_topic_score_gemma":0.000003439295,"domain_scores_codex":[0.9988629,0.0001238347,0.0001960822,0.0003161931,0.0003724007,0.0001285416],"domain_scores_gemma":[0.9995206,0.0001741175,0.00009964652,0.00008004526,0.00009117959,0.00003443199],"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":[0.00006137876,0.00002631341,0.0000291915,0.0000964446,0.000004545864,4.420016e-7,0.0001203569,0.0006100694,0.8987514,0.0001082881,0.0001267991,0.1000647],"study_design_scores_gemma":[0.0002845895,0.0001046905,0.003685654,0.00003660344,0.00003936074,0.0000392045,0.00005783411,0.4743157,0.5203365,0.00009334069,0.0009412314,0.00006527395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9652486,0.00008395669,0.03274984,0.0005766782,0.0003707515,0.0006538655,0.00001840553,0.00008647171,0.0002113925],"genre_scores_gemma":[0.9991447,0.00001250873,0.0003698138,0.0001024085,0.0001495074,0.00009034709,0.0000038618,0.0000156088,0.0001112569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4737057,"threshold_uncertainty_score":0.4084383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06126372352500752,"score_gpt":0.3256512291918584,"score_spread":0.2643875056668509,"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."}}