{"id":"W4388478170","doi":"10.18280/ria.370518","title":"Efficient Feature Selection Using CNN, VGG16 and PCA for Breast Cancer Ultrasound Detection","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"AI in cancer detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Breast cancer; Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Computer science; Cancer detection; Cancer; Medicine; Internal medicine","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":[0.0004329816,0.0001911296,0.0001736696,0.0002499667,0.0005384734,0.0001932127,0.0002817309,0.0001351428,0.00002333734],"category_scores_gemma":[0.00006612561,0.0001988903,0.0000845552,0.00164464,0.0000669617,0.0002103972,0.00009112959,0.0001932207,0.00004870199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002894951,"about_ca_system_score_gemma":0.00006641009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001196561,"about_ca_topic_score_gemma":0.0001067384,"domain_scores_codex":[0.9983997,0.00004819795,0.0002636361,0.0006561837,0.0001968005,0.0004354518],"domain_scores_gemma":[0.9990062,0.0002230132,0.0001374988,0.0003274281,0.0002099004,0.00009595573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004952158,0.00003836867,0.0003880196,0.0001160047,0.00002611,0.00000210799,0.001019079,0.5645577,0.1888892,0.0007098931,0.0004075555,0.2437965],"study_design_scores_gemma":[0.00004966678,0.00007033933,0.0008052699,0.00006337425,0.00001485693,0.000154065,0.0001556418,0.8604571,0.1361937,0.0006514704,0.001177975,0.0002065261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1903778,0.0001736847,0.8067167,0.0006679328,0.001192132,0.0004355923,0.00001682408,0.0003625556,0.00005679624],"genre_scores_gemma":[0.9947329,0.0001195156,0.00392538,0.00006764416,0.0003359531,0.0001135519,0.00000191926,0.00002694358,0.0006761613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8043552,"threshold_uncertainty_score":0.811051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03562252867245017,"score_gpt":0.2911551333030336,"score_spread":0.2555326046305834,"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."}}