{"id":"W3196525672","doi":"10.17762/de.vi.3967","title":"Feature Extraction of Melanoma Data using Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"Design Engineering","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology); Melanoma; Feature extraction; Image segmentation; Segmentation; Binary image; Kernel (algebra); Feature (linguistics); Skin cancer; Image processing; Image (mathematics); Mathematics; Cancer; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001680124,0.00009360394,0.0001657349,0.0001122971,0.00002880456,0.00001249003,0.00005917785,0.00006158342,0.00005381686],"category_scores_gemma":[0.0001774847,0.00009654879,0.00003309665,0.0001914447,0.000005570869,0.00007283748,0.00006950337,0.0002075147,0.000002245715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004921962,"about_ca_system_score_gemma":0.00002781936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001900399,"about_ca_topic_score_gemma":0.000002335997,"domain_scores_codex":[0.9994334,0.00002246671,0.0001150977,0.0001831948,0.0001316055,0.0001142624],"domain_scores_gemma":[0.9994928,0.00005236008,0.00004595376,0.0003158695,0.00004895904,0.00004406441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003555793,0.00003223582,0.0002800548,0.0001422297,0.00007502212,0.000346802,0.00002918447,0.004090275,0.9889545,0.00003943327,0.0002102695,0.005764376],"study_design_scores_gemma":[0.0002274192,0.00009152378,0.000306255,0.0001457922,0.0001189036,0.001786066,0.00003980502,0.3691644,0.5692281,0.000002294965,0.05876877,0.0001206973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02953335,0.0008449751,0.9681008,0.000241232,0.0002356689,0.0002532798,0.000005759991,0.0003380325,0.0004468671],"genre_scores_gemma":[0.6761068,0.0001460806,0.3216793,0.00005115271,0.0001884638,0.000005417526,0.00009492292,0.0000474256,0.001680479],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6465734,"threshold_uncertainty_score":0.3937145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04438226549041522,"score_gpt":0.2815732040621531,"score_spread":0.2371909385717379,"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."}}