{"id":"W4311164285","doi":"10.18280/ts.390524","title":"Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Support vector machine; Skin cancer; Pattern recognition (psychology); Thresholding; Skin lesion; Computer science; Random forest; Classifier (UML); Melanoma; Feature vector; Contextual image classification; Machine learning; Cancer; Medicine; Image (mathematics); Dermatology","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":[0.0004496777,0.0005650416,0.0009196421,0.001462895,0.0002794474,0.00065133,0.0005804993,0.0005570159,0.001928382],"category_scores_gemma":[0.00103145,0.0002012523,0.0006627938,0.0008574238,0.0001357961,0.0005951143,0.0003420556,0.0004601703,0.001190127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003174834,"about_ca_system_score_gemma":0.0004386745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002398969,"about_ca_topic_score_gemma":0.001975205,"domain_scores_codex":[0.9996129,0.00005659766,0.00003254242,0.00008005867,0.0001615417,0.00005637969],"domain_scores_gemma":[0.9995922,0.0001290997,0.00004638597,0.0000409002,0.0001751549,0.00001622791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002348309,0.0002110933,0.002560879,0.0001276215,0.00005157983,0.0001720637,0.00004076905,0.04702096,0.04595005,0.0009224528,0.002640737,0.9000671],"study_design_scores_gemma":[0.00001029193,0.00009401206,0.003142278,0.00001301602,0.00002115765,0.00021381,0.00003813272,0.9790424,0.01506818,0.0008863719,0.001456211,0.00001414838],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07428221,0.001074651,0.9186927,0.0001609162,0.00008535753,0.0001371828,0.0002298106,0.002875737,0.002461515],"genre_scores_gemma":[0.6103144,0.0006097218,0.3839116,0.00009100648,0.0000715731,0.0001414021,0.0009021448,0.00007171283,0.003886365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002398969,"threshold_uncertainty_score":0.00645113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0408038922297962,"score_gpt":0.244400205918252,"score_spread":0.2035963136884558,"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."}}