{"id":"W4378803851","doi":"10.20944/preprints202305.2209.v1","title":"Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"AI in cancer detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Radiological Society of North America","keywords":"Convolutional neural network; Mammography; Artificial intelligence; Computer science; Feature selection; Breast cancer; Pattern recognition (psychology); Classifier (UML); Feature extraction; Artificial neural network; Feature (linguistics); 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":[0.0005548222,0.001142829,0.0009523219,0.001562676,0.0002930357,0.0006251626,0.0009248058,0.0006367624,0.001107085],"category_scores_gemma":[0.0009823748,0.0003463467,0.0008351218,0.001176637,0.0002252625,0.0006607009,0.0006205613,0.0005061134,0.0005483995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007334282,"about_ca_system_score_gemma":0.0007474971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007769875,"about_ca_topic_score_gemma":0.01020215,"domain_scores_codex":[0.9996469,0.00004394062,0.00002041652,0.0001053047,0.0001067785,0.00007674131],"domain_scores_gemma":[0.9997812,0.00005148258,0.0000314223,0.00003254362,0.00008719895,0.00001603154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003064827,0.0002678296,0.007865748,0.0001597983,0.0002220765,0.0002474839,0.00005812585,0.04564907,0.07074845,0.001117991,0.006294069,0.8670629],"study_design_scores_gemma":[0.00002295526,0.0001096317,0.007672796,0.00002109799,0.00009956877,0.0003196024,0.00003817212,0.9488832,0.03730055,0.0018262,0.003681832,0.00002442762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1352563,0.002515075,0.8524349,0.0006365106,0.0001640352,0.0002834312,0.0009289227,0.004121873,0.00365893],"genre_scores_gemma":[0.5946898,0.001379105,0.3946969,0.00036497,0.0001869841,0.0002654925,0.002359279,0.0001714606,0.005885963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007769875,"threshold_uncertainty_score":0.01544929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05680739561498406,"score_gpt":0.3013425763835118,"score_spread":0.2445351807685277,"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."}}