{"id":"W4377832607","doi":"10.18280/ts.400242","title":"Empirical Investigations to Skin Lesion Detection Using DenseNet Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Lesion; Artificial neural network; Medicine; Pathology","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":[0.002795409,0.0008383001,0.0004732686,0.001129841,0.0003415322,0.0006583419,0.001079388,0.0007581955,0.001454681],"category_scores_gemma":[0.009416112,0.0002270252,0.0004742477,0.0007073137,0.00059602,0.001149979,0.0005398088,0.0008586412,0.0002690956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016513,"about_ca_system_score_gemma":0.0004904299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01224437,"about_ca_topic_score_gemma":0.01445385,"domain_scores_codex":[0.9989518,0.0004067648,0.00007835374,0.0002360925,0.0002004033,0.0001265617],"domain_scores_gemma":[0.994437,0.003757555,0.0004081329,0.0005690322,0.0006871037,0.0001412026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001638404,0.001779071,0.4079134,0.001275558,0.000755068,0.001206775,0.0003929815,0.3233609,0.00588598,0.003896596,0.01939921,0.2324961],"study_design_scores_gemma":[0.0000311128,0.0004187097,0.06920525,0.0001016097,0.0001265965,0.0005524313,0.0003562914,0.9188059,0.00502291,0.002670457,0.002677677,0.00003098514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766735,0.003365528,0.01452219,0.0007454811,0.0001050978,0.00006336676,0.001586494,0.0002009092,0.0027374],"genre_scores_gemma":[0.9919322,0.0004494902,0.003933228,0.00006035399,0.00003757538,0.00001879867,0.002566009,0.00001456774,0.0009877752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01224437,"threshold_uncertainty_score":0.02434623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08167419379772947,"score_gpt":0.3182772917798258,"score_spread":0.2366030979820964,"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."}}