{"id":"W4235038263","doi":"10.35940/ijeat.b4916.129219","title":"Skin Cancer Diagnostic using Machine Learning Techniques - Shearlet Transform and Naïve Bayes Classifier","year":2019,"lang":"en","type":"article","venue":"International Journal of Engineering and Advanced Technology","topic":"AI in cancer detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Shearlet; Naive Bayes classifier; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Bayes classifier; Computer science; Bayes' theorem; Melanoma diagnosis; Contextual image classification; Mathematics; Melanoma; Support vector machine; Medicine; Image (mathematics); Bayesian probability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001556818,0.0006875133,0.0009630935,0.002758874,0.0004569826,0.001049246,0.0006960101,0.00102553,0.001341978],"category_scores_gemma":[0.003463875,0.0003242374,0.0008682139,0.0008509795,0.0003792853,0.001192544,0.0003487015,0.0006717684,0.000703263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005266389,"about_ca_system_score_gemma":0.0008592223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002741102,"about_ca_topic_score_gemma":0.002046109,"domain_scores_codex":[0.9987619,0.0002212465,0.0001505908,0.000220464,0.0005372406,0.000108615],"domain_scores_gemma":[0.998804,0.0004877749,0.0001227876,0.00006481681,0.000482701,0.00003795583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004849101,0.0003828712,0.0218431,0.000411322,0.0002101373,0.0005200438,0.0001766773,0.05536804,0.04147115,0.005300019,0.005794005,0.8680377],"study_design_scores_gemma":[0.00003506524,0.0002619405,0.008658339,0.00009490277,0.0001078817,0.001006713,0.0001134799,0.9553198,0.02436323,0.006662811,0.00331644,0.00005927445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09092101,0.002643456,0.8998315,0.0006549563,0.0002901543,0.0002181792,0.0003166796,0.001551832,0.003572305],"genre_scores_gemma":[0.6376323,0.001631105,0.3555866,0.0002779427,0.0002800051,0.0002028752,0.0008282426,0.00006273832,0.003498102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002758874,"threshold_uncertainty_score":0.008233309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004429185020775231,"score_gpt":0.2428739220932633,"score_spread":0.238444737072488,"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."}}