{"id":"W4382394973","doi":"10.18280/ts.400337","title":"Diagnosis of Melanoma Using Differential Evolution Optimized Artificial Neural Network","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Differential evolution; Differential (mechanical device); Artificial intelligence; Computer science; Melanoma; Biology; Engineering; Cancer research","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":[0.0005137334,0.0004695587,0.0004430227,0.0007373991,0.0002024724,0.0005100438,0.0004971964,0.000690549,0.0003949593],"category_scores_gemma":[0.001387751,0.0002045276,0.0003792417,0.0003228293,0.0002086233,0.0002945626,0.0002924937,0.0003708205,0.00008907489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006796388,"about_ca_system_score_gemma":0.0004172883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004862032,"about_ca_topic_score_gemma":0.00378692,"domain_scores_codex":[0.9998017,0.0000437981,0.00001785366,0.0000561299,0.00005444927,0.00002601069],"domain_scores_gemma":[0.9995974,0.0001903673,0.00005692232,0.00001646723,0.0001221969,0.00001676766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002117936,0.0001410901,0.007586133,0.00006405376,0.00007237664,0.0002249538,0.00004310701,0.8420464,0.01144689,0.0009288166,0.0007816025,0.1364527],"study_design_scores_gemma":[0.000002267277,0.00001557048,0.0003212283,0.000002264753,0.000003393759,0.00001548206,0.000002817889,0.9987556,0.0006663324,0.0001558724,0.00005753662,0.000001690573],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3833585,0.001362131,0.6091087,0.0006084192,0.0001168976,0.000112907,0.0001282659,0.0007334253,0.004470626],"genre_scores_gemma":[0.9353256,0.0002155589,0.06274348,0.0001156631,0.0000210469,0.00004382319,0.0001344969,0.00001321286,0.001387119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004862032,"threshold_uncertainty_score":0.009667456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03893867064369137,"score_gpt":0.2594186406760663,"score_spread":0.220479970032375,"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."}}