{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004484579,0.0001887582,0.0002141936,0.0002462284,0.0004682225,0.00004152701,0.00009876226,0.00003891957,0.002693182],"category_scores_gemma":[0.00001276874,0.0001827771,0.0001397657,0.0003036872,0.00003865185,0.00001988862,0.00007737824,0.0003983685,0.00001823347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003787874,"about_ca_system_score_gemma":0.00005914578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003398869,"about_ca_topic_score_gemma":0.000004048406,"domain_scores_codex":[0.9983591,0.0001702297,0.0002490315,0.0003716768,0.000584416,0.0002655579],"domain_scores_gemma":[0.9994912,0.00003376567,0.0001403147,0.0001898034,0.00003513991,0.000109763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004148924,0.004502766,0.01009478,0.0006205444,0.0006412413,0.0008821628,0.002305147,0.4703117,0.4439853,0.001924014,0.003639015,0.05694443],"study_design_scores_gemma":[0.002081067,0.0005580357,0.003862811,0.00001849489,0.0001666947,0.0001990836,0.0008250204,0.9587324,0.002018087,0.000005056555,0.0313342,0.000199031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9431886,0.0001951489,0.04847296,0.002263337,0.0002577347,0.00128293,0.00003001678,0.0002785293,0.004030727],"genre_scores_gemma":[0.9949684,0.000001402722,0.003125126,0.0004026867,0.0001412999,0.0000985524,0.0001860391,0.00003572463,0.001040767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4884208,"threshold_uncertainty_score":0.9982185,"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."}}