{"id":"W2588546842","doi":"10.21474/ijar01/3099","title":"Severity Analysis of Cervical Cancer in Pap Smear Images by using EEETCM, ERSTCM &amp; CFE method based Texture Features and Hybrid Kernel based Support Vector Machine Classifier.","year":2016,"lang":"en","type":"article","venue":"International Journal of Advanced Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women's College Hospital","funders":"","keywords":"Support vector machine; Cervical cancer; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Kernel (algebra); Gynecology; Medicine; Computer science; Mathematics; Cancer; Internal medicine; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002142727,0.0001554177,0.000377327,0.001333187,0.00008132944,0.00009763088,0.001043074,0.00008813313,0.0002276582],"category_scores_gemma":[0.0004653829,0.000116997,0.0001760326,0.001003002,0.0002006824,0.000698718,0.0001940772,0.0006119826,8.813238e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008970999,"about_ca_system_score_gemma":0.0005900272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005230813,"about_ca_topic_score_gemma":0.0004794981,"domain_scores_codex":[0.9963768,0.0006149776,0.000534632,0.0003759267,0.001767252,0.0003303848],"domain_scores_gemma":[0.9966501,0.001001529,0.0003853196,0.0002970468,0.001489583,0.0001764496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002625968,0.0005415126,0.04859984,0.00009114963,0.001574684,0.0002934247,0.000525644,0.04798483,0.4250833,0.0002551553,0.00466094,0.4677635],"study_design_scores_gemma":[0.009483264,0.0008168002,0.2303495,0.001150145,0.0002894944,0.0002526024,0.0001707662,0.488625,0.2422293,0.002772257,0.02287634,0.0009845255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0781841,0.0006634626,0.9066643,0.01340179,0.0005611806,0.0001612693,0.0002525255,0.0000171929,0.00009424594],"genre_scores_gemma":[0.942527,0.0003005917,0.05667526,0.0001654201,0.0001256483,0.000007290365,0.000004614868,0.00001522815,0.0001789683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8643429,"threshold_uncertainty_score":0.4771001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04058341667895639,"score_gpt":0.4176277151781351,"score_spread":0.3770442984991787,"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."}}