{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004607557,0.001208553,0.0007899013,0.001528898,0.0002744222,0.0007087114,0.0009219839,0.000601585,0.001134895],"category_scores_gemma":[0.000980211,0.0003154835,0.0009799504,0.001282694,0.0002573222,0.0007075797,0.0004991319,0.0005773269,0.0006919238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000824197,"about_ca_system_score_gemma":0.0008664341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01220398,"about_ca_topic_score_gemma":0.01041541,"domain_scores_codex":[0.9997391,0.00002527018,0.00001784512,0.00006865216,0.00007873825,0.00007029133],"domain_scores_gemma":[0.9998139,0.00004090841,0.00002742697,0.00002755112,0.00007693712,0.00001335774],"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":[0.0003727492,0.0002199762,0.007525771,0.0001709175,0.0001670086,0.0003298384,0.00007707256,0.1102224,0.04428023,0.001419151,0.01114944,0.8240655],"study_design_scores_gemma":[0.00002764719,0.0001578523,0.006441023,0.00003047985,0.00006897892,0.0002483382,0.00004914464,0.9577699,0.02901031,0.00192421,0.004245522,0.00002652108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4169108,0.004610025,0.5543116,0.0009658236,0.0004098613,0.0004142432,0.002408456,0.01264657,0.007322612],"genre_scores_gemma":[0.8190371,0.001262868,0.1677959,0.0003243412,0.00007951425,0.0002383562,0.004047872,0.0002184702,0.006995673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01220398,"threshold_uncertainty_score":0.02426583,"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."}}