{"id":"W4401935122","doi":"10.1109/tfuzz.2024.3450000","title":"FSCNN: Fuzzy Channel Filter-Based Separable Convolution Neural Networks for Medical Imaging Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Convolution (computer science); Separable space; Artificial intelligence; Computer science; Medical imaging; Pattern recognition (psychology); Artificial neural network; Channel (broadcasting); Fuzzy logic; Filtering theory; Filter (signal processing); Computer vision; Mathematics; Telecommunications","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":[0.0006721766,0.0006692681,0.0005471642,0.0005573189,0.0002231985,0.0004543915,0.001083794,0.0008538617,0.001905572],"category_scores_gemma":[0.001534458,0.0002360323,0.0005881913,0.0005212367,0.0004551558,0.0008506184,0.0005975546,0.0009573705,0.0005664126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008264828,"about_ca_system_score_gemma":0.001168771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01170216,"about_ca_topic_score_gemma":0.01378621,"domain_scores_codex":[0.9997588,0.00003751416,0.00001425436,0.00005743606,0.00009495097,0.00003700014],"domain_scores_gemma":[0.9997165,0.0000940586,0.00002598254,0.00003653643,0.0001086546,0.00001821157],"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":[0.0002857035,0.0001096111,0.001553947,0.000140004,0.0001445722,0.0001366264,0.00006327027,0.3949542,0.02039343,0.01252463,0.008965379,0.5607287],"study_design_scores_gemma":[0.0000052576,0.00002081417,0.0001910106,0.000006918014,0.000009856045,0.00003494592,0.000002986596,0.9931877,0.002800347,0.002705224,0.00102813,0.000006869135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01842844,0.0008523651,0.9766663,0.0002560241,0.0001105579,0.00005393554,0.0002116602,0.001370341,0.002050416],"genre_scores_gemma":[0.5300607,0.001197564,0.4583635,0.0004964672,0.0001452191,0.0002114393,0.001081991,0.0001682235,0.008274844],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01170216,"threshold_uncertainty_score":0.02326804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02459694956517445,"score_gpt":0.2627372034576854,"score_spread":0.238140253892511,"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."}}