{"id":"W1120651397","doi":"10.1007/s10916-015-0311-6","title":"A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Systems","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Preprocessor; Artificial intelligence; Segmentation; Feature extraction; Feature (linguistics); Inference engine; Focus (optics); Pattern recognition (psychology); Computer vision; Fuzzy logic; Image processing; Inference; Image (mathematics)","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002849973,0.0001142444,0.0003069123,0.0001250177,0.0001216672,0.0001900227,0.0003747123,0.0001172127,0.000007101576],"category_scores_gemma":[0.00878473,0.0000831977,0.00008910161,0.0002986529,0.00008761602,0.0002842248,0.00002444459,0.0002690891,0.00001993969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000248146,"about_ca_system_score_gemma":0.0004607935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001484754,"about_ca_topic_score_gemma":7.975696e-7,"domain_scores_codex":[0.9970899,0.0003161852,0.0007319576,0.0001840682,0.001451622,0.0002263199],"domain_scores_gemma":[0.9978703,0.0005265658,0.0006518679,0.000122273,0.0003186381,0.0005103813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001063303,0.0008083613,0.0003441674,0.003163584,0.00005124919,0.001511624,0.007114076,0.0001356565,0.6181332,0.01188289,0.2769602,0.0788317],"study_design_scores_gemma":[0.01629755,0.003453898,0.0004349723,0.01719895,0.0001121926,0.0418499,0.05061262,0.1823046,0.1866882,0.0006519011,0.4988641,0.001531153],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6754236,0.007173865,0.1967637,0.07374419,0.03410828,0.002666836,0.00001739523,0.0006464863,0.009455636],"genre_scores_gemma":[0.9977483,0.00001160492,0.0001621802,0.0004720323,0.001397978,0.00002221695,3.435923e-7,0.00002231422,0.0001631033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4314451,"threshold_uncertainty_score":0.9995647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07998208814671419,"score_gpt":0.3401319944632105,"score_spread":0.2601499063164963,"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."}}