{"id":"W4229443674","doi":"10.18280/ts.390214","title":"A New Hybrid Breast Cancer Diagnosis Model Using Deep Learning Model and ReliefF","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"AI in cancer detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Support vector machine; Computer science; Machine learning; Convolutional neural network; Naive Bayes classifier; Deep learning; Breast cancer; Pattern recognition (psychology); Transfer of learning; Classifier (UML); Artificial neural network; Cancer; 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":[0.0002567764,0.0001664517,0.0001519151,0.0001191931,0.000542969,0.0001234957,0.0003893061,0.00002070128,0.0002709741],"category_scores_gemma":[0.000002639791,0.0001880399,0.00005773214,0.0002453535,0.00002192561,0.0004651475,0.0004249424,0.0002807095,0.000001648338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004372918,"about_ca_system_score_gemma":0.0001986371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004085983,"about_ca_topic_score_gemma":0.00002443305,"domain_scores_codex":[0.9984064,0.00007090173,0.0002249773,0.0004868587,0.0004962492,0.0003146221],"domain_scores_gemma":[0.9994906,0.00003613173,0.0001189272,0.0001829146,0.00003879969,0.0001326379],"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":[0.00002190007,0.00002946295,0.0007560157,0.000008733235,0.00001947254,0.000005016075,0.0007829397,0.9058656,0.0009906988,0.0003433287,0.0007922158,0.09038463],"study_design_scores_gemma":[0.0005211077,0.00008230062,0.0003362869,0.00001530901,0.00002381422,0.00006384146,0.00003159967,0.9958691,0.0004251867,0.002018982,0.000391475,0.0002209974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1340541,0.0003487652,0.8643638,0.000728764,0.0001056655,0.0001719559,0.00001766523,0.0001383819,0.00007087972],"genre_scores_gemma":[0.9606712,0.00008120279,0.03828004,0.0004370203,0.0001215437,0.0002551212,0.000001937877,0.0000225416,0.0001293297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8266172,"threshold_uncertainty_score":0.7668045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322270039703138,"score_gpt":0.2461706726459423,"score_spread":0.2229479722489109,"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."}}