{"id":"W4400940545","doi":"10.1063/5.0221423","title":"The design of an efficient bioinspired CNN model for automated malaria detection in blood smear images","year":2024,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Malaria; Blood smear; Artificial intelligence; Computer vision; Medicine; Immunology","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":[0.0003314486,0.0006883092,0.0004952623,0.0003227367,0.0002376183,0.0005358635,0.00147873,0.0008205214,0.002057202],"category_scores_gemma":[0.0005563815,0.0004187793,0.000533178,0.0002867042,0.0001832323,0.0006198576,0.0004688413,0.0006678635,0.001035168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00089787,"about_ca_system_score_gemma":0.001341867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009144119,"about_ca_topic_score_gemma":0.01340261,"domain_scores_codex":[0.9998736,0.000008926753,0.000006058997,0.00004383581,0.00003742923,0.00003002376],"domain_scores_gemma":[0.9998307,0.00002085113,0.00001611272,0.00001439656,0.0001048092,0.00001319182],"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":[0.0002767633,0.0002493219,0.003577517,0.0002453474,0.00020598,0.000223999,0.00005126221,0.3286012,0.136338,0.004377138,0.009061379,0.5167921],"study_design_scores_gemma":[0.000005219192,0.00005022209,0.0003722032,0.000007123869,0.00002707483,0.00005015766,0.000005033964,0.9824311,0.01527971,0.0003820076,0.001383953,0.000006210901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04652254,0.0008191729,0.9452775,0.000429682,0.0002490859,0.0001704977,0.0003501456,0.002433195,0.003748224],"genre_scores_gemma":[0.6566219,0.0009612592,0.324964,0.0006580975,0.0001125401,0.000370517,0.001141857,0.0001875638,0.01498215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009144119,"threshold_uncertainty_score":0.0181818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02609686252998054,"score_gpt":0.2623203150676742,"score_spread":0.2362234525376936,"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."}}