{"id":"W4400042735","doi":"10.18280/ts.410302","title":"DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Convolution (computer science); Artificial neural network; Deep learning; Breast cancer; Pattern recognition (psychology); Convolutional neural network; Algorithm; Machine learning; 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":[0.0004848604,0.0006403631,0.0006269442,0.0008861088,0.0002656307,0.0005292257,0.001290378,0.0008108242,0.001879929],"category_scores_gemma":[0.0006945418,0.0002471977,0.0006025789,0.0006076272,0.0002642943,0.000784229,0.0005750708,0.000855592,0.0007315128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008918938,"about_ca_system_score_gemma":0.001185033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0139479,"about_ca_topic_score_gemma":0.01335692,"domain_scores_codex":[0.9997695,0.00002884808,0.00001271568,0.0000584102,0.0000860472,0.00004438201],"domain_scores_gemma":[0.9998735,0.00003002742,0.0000139332,0.00001170614,0.00005746478,0.00001333175],"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":[0.0001292317,0.0001366124,0.002846727,0.0001378294,0.0001103219,0.0001419916,0.00005386421,0.2085716,0.01369076,0.00878025,0.01076819,0.7546325],"study_design_scores_gemma":[0.000007496893,0.00003433724,0.0004262703,0.000009284729,0.00001137935,0.00007005939,0.000006574861,0.9906698,0.002839457,0.002496691,0.003419136,0.000009456682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008714754,0.001054274,0.9856288,0.0002385614,0.00006892974,0.00006187384,0.000231824,0.002302757,0.001698245],"genre_scores_gemma":[0.3181782,0.001802723,0.6668597,0.0005287807,0.0001302822,0.0003111243,0.001712559,0.0002620799,0.01021454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0139479,"threshold_uncertainty_score":0.02773345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667783704448092,"score_gpt":0.2790454900633262,"score_spread":0.2523676530188453,"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."}}