{"id":"W3109770947","doi":"10.18280/ria.340506","title":"Lightweight Deep Learning for Malaria Parasite Detection Using Cell-Image of Blood Smear Images","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Malaria; Blood smear; Transfer of learning; Parasite hosting; Artificial intelligence; Deep learning; Infectious disease (medical specialty); Computer science; Anopheles; Population; Malarial parasites; Pattern recognition (psychology); Disease; Machine learning; Immunology; Biology; Plasmodium falciparum; Medicine; Pathology; Environmental health","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":[0.0002872087,0.0005957786,0.0004177466,0.0004212883,0.0001662322,0.0003634153,0.0007551688,0.0004496042,0.001621965],"category_scores_gemma":[0.0006803345,0.0002088445,0.0003891573,0.0003367111,0.0001596771,0.0006039384,0.0004501585,0.0005833997,0.000503715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005718294,"about_ca_system_score_gemma":0.0004936426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007454611,"about_ca_topic_score_gemma":0.008957355,"domain_scores_codex":[0.9998814,0.0000140593,0.000006251939,0.00002948397,0.00003622718,0.00003258859],"domain_scores_gemma":[0.9998519,0.00003817202,0.00001940691,0.0000195184,0.00005704676,0.00001388265],"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":[0.0005555033,0.0003985085,0.007689918,0.000206995,0.0001754644,0.0003064979,0.00008216287,0.1748242,0.06221808,0.002377245,0.008963988,0.7422013],"study_design_scores_gemma":[0.000007193822,0.00007054213,0.001199882,0.000009323642,0.00001786414,0.00005543663,0.00001141854,0.9874952,0.009510483,0.0007454313,0.0008712672,0.000005963518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3409895,0.002876489,0.6422986,0.000740389,0.0003189139,0.000158101,0.0009124946,0.004543468,0.007162013],"genre_scores_gemma":[0.9000978,0.000849458,0.0903088,0.0002900409,0.00007231873,0.00008974611,0.00134886,0.00005972219,0.006883206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007454611,"threshold_uncertainty_score":0.01482242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02882648910005333,"score_gpt":0.2597074889007104,"score_spread":0.2308809998006571,"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."}}