{"id":"W2943148339","doi":"10.5539/cis.v12n2p146","title":"Image Processing & Neural Network Based Breast Cancer Detection","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Breast cancer; Artificial neural network; Mammography; Artificial intelligence; Image processing; Pattern recognition (psychology); Wavelet; Transformation (genetics); Cancer; Computer vision; Computer-aided diagnosis; Machine learning; Image (mathematics); 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":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0004924018,0.0001140745,0.00009836208,0.0002132026,0.000391774,0.001057312,0.0005394822,0.00003492518,0.00001600203],"category_scores_gemma":[0.000005321574,0.0001022915,0.00002422106,0.001353031,0.0001388719,0.01731101,0.0002301741,0.0001202197,0.00007519478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001136808,"about_ca_system_score_gemma":0.0001779486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000224435,"about_ca_topic_score_gemma":0.000004265405,"domain_scores_codex":[0.9987504,0.00001947704,0.0002359368,0.0002609463,0.0004329316,0.0003002784],"domain_scores_gemma":[0.9990919,0.00002277643,0.0001604436,0.0003017605,0.0003244155,0.00009872176],"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":[0.000009491738,0.000005115535,0.001546199,0.00004941564,0.000001294171,1.134899e-7,0.0004780125,0.02386009,0.001015069,0.0003024054,0.0002024448,0.9725304],"study_design_scores_gemma":[0.0002056085,0.00003182532,0.05072063,0.00003019755,0.000001307875,0.0000351661,0.000004787968,0.9442959,0.0004237061,0.00006648019,0.004046977,0.0001373439],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05468853,0.00002553954,0.9423622,0.0004674028,0.001425444,0.0001770395,0.000001558143,0.0001853294,0.0006668948],"genre_scores_gemma":[0.9428546,0.00000818848,0.0553247,0.001620043,0.0001650844,0.00001475619,0.000001019946,0.000003168318,0.000008431133],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.972393,"threshold_uncertainty_score":0.9999797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009275305412454004,"score_gpt":0.2445875501897998,"score_spread":0.2353122447773458,"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."}}