{"id":"W4384557826","doi":"10.3390/info14070410","title":"Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection","year":2023,"lang":"en","type":"article","venue":"Information","topic":"AI in cancer detection","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial intelligence; Computer science; Random forest; Support vector machine; Convolutional neural network; Mammography; Feature selection; Pattern recognition (psychology); Classifier (UML); Breast cancer; Feature extraction; Artificial neural network; Machine learning; Cancer; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004777812,0.001043528,0.0009202855,0.001597318,0.0002722389,0.0005912943,0.0009178875,0.0005772086,0.001101933],"category_scores_gemma":[0.0008252159,0.000316461,0.0008189947,0.001197929,0.0001892168,0.0006527633,0.0005831562,0.0004462549,0.0005207629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007131253,"about_ca_system_score_gemma":0.0007139712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009202011,"about_ca_topic_score_gemma":0.01205048,"domain_scores_codex":[0.9996884,0.00003475272,0.00001778859,0.00009065602,0.00009530435,0.00007308039],"domain_scores_gemma":[0.9998254,0.00003609115,0.00002549723,0.00002544251,0.00007349497,0.00001398235],"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":[0.0002745696,0.0002553967,0.007403348,0.0001472046,0.0002076537,0.0002233042,0.00004837331,0.03947702,0.05232618,0.0008909641,0.00665227,0.8920937],"study_design_scores_gemma":[0.000021474,0.0001207843,0.007915117,0.00002285901,0.0001077939,0.0003319492,0.00004008658,0.9551617,0.03053767,0.001592824,0.004122201,0.00002557314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1535826,0.003439716,0.8313792,0.0006799812,0.0002024275,0.0003018458,0.001346164,0.004718408,0.004349666],"genre_scores_gemma":[0.6499807,0.001679582,0.3384986,0.0004082193,0.0001872909,0.0002583643,0.002951728,0.0001569034,0.005878787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009202011,"threshold_uncertainty_score":0.0182969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005280063777756617,"score_gpt":0.2072418453120105,"score_spread":0.2019617815342539,"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."}}