{"id":"W4405861132","doi":"10.1007/s13755-024-00327-1","title":"Csec-net: a novel deep features fusion and entropy-controlled firefly feature selection framework for leukemia classification","year":2024,"lang":"en","type":"article","venue":"Health Information Science and Systems","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Computer science; Preprocessor; Feature selection; Support vector machine; Deep learning; Feature extraction; Entropy (arrow of time); Contextual image classification; Transfer of learning; Machine learning; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001452844,0.0001473763,0.0002222489,0.0004361023,0.0006456453,0.003664315,0.0002874842,0.00007827129,4.160701e-7],"category_scores_gemma":[0.0004445129,0.0001143608,0.00003830636,0.001052844,0.0001145337,0.007238719,0.00007264119,0.00013805,0.000009022868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003012108,"about_ca_system_score_gemma":0.0009174798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000594427,"about_ca_topic_score_gemma":0.00000435219,"domain_scores_codex":[0.99822,0.00002578989,0.0004217328,0.0003298555,0.0006402248,0.0003624592],"domain_scores_gemma":[0.9987437,0.0001838331,0.0002295627,0.0002140783,0.0003785014,0.0002503887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006852742,0.00002778553,0.0002111163,0.001281921,0.00001629534,3.884208e-7,0.005127758,0.0002564383,0.0005416491,0.8035175,0.007220504,0.1817301],"study_design_scores_gemma":[0.0007464751,0.0001395178,0.00498544,0.0002874108,0.000007222885,0.0000807205,0.0004633903,0.9636475,0.00003838776,0.001384545,0.02806217,0.0001571708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01210986,0.004509808,0.9712092,0.006875323,0.001794131,0.002107305,0.00003554617,0.0004692972,0.0008895574],"genre_scores_gemma":[0.9829931,0.0001381655,0.0149232,0.001478528,0.0001303384,0.0002059664,0.00001904372,0.000006590784,0.0001050633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9708833,"threshold_uncertainty_score":0.99737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352767865465088,"score_gpt":0.3082299839851904,"score_spread":0.2847023053305395,"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."}}