{"id":"W4387251285","doi":"10.1109/aic57670.2023.10263933","title":"Automatic White Blood Cell Classification Using Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Convolutional neural network; Overfitting; Computer science; Artificial intelligence; Pattern recognition (psychology); White blood cell; Peripheral blood; Deep learning; Feature extraction; Anemia; Artificial neural network; Machine learning; Medicine; Internal 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001180808,0.0001100608,0.00009432939,0.00009475752,0.0001271701,0.0003690627,0.0004990193,0.00002396401,0.00003401591],"category_scores_gemma":[0.0000225633,0.0001069563,0.0000658108,0.0009221173,0.00004808511,0.0009257403,0.0002316496,0.00004972489,0.0003671507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002005436,"about_ca_system_score_gemma":0.00008705497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006095581,"about_ca_topic_score_gemma":9.326288e-7,"domain_scores_codex":[0.9988484,0.00003195144,0.0001957033,0.0002969687,0.0002843695,0.0003426174],"domain_scores_gemma":[0.9993103,0.00006355812,0.00006996617,0.000381903,0.00006003153,0.0001142795],"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":[0.000009663287,0.001563718,0.21813,0.0003284884,0.0001998275,0.0002549092,0.0006283841,0.1945742,0.007815459,0.3507484,0.1748274,0.05091963],"study_design_scores_gemma":[0.0001798931,0.00001370998,0.045021,0.00001052845,0.00001601951,0.00001294575,0.00001248821,0.9510456,0.0001105317,0.003274978,0.0001708919,0.0001313969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9065183,0.0001370203,0.06478702,0.00133295,0.0009191055,0.0002604261,0.000009110381,0.003346824,0.02268922],"genre_scores_gemma":[0.967568,0.000001068633,0.03079962,0.000330551,0.00009214569,0.000008388683,0.00001851633,0.00001109692,0.001170657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7564714,"threshold_uncertainty_score":0.4719101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03760237403897718,"score_gpt":0.2581706693709998,"score_spread":0.2205682953320226,"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."}}