{"id":"W4413176141","doi":"10.18280/ts.420404","title":"MRI Image Linked Pixel Edge Segmentation with Least Correlated Weight Factor for Lung Tumor Identification Using Machine Learning Technique","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Identification (biology); Segmentation; Pixel; Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution; Edge detection; Image segmentation; Computer science; Computer vision; Lung; Image (mathematics); Image processing; Medicine; Internal medicine; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004730354,0.0005030135,0.0005572734,0.001427,0.0003869003,0.0005686797,0.000541787,0.00080403,0.001312128],"category_scores_gemma":[0.0008674295,0.0002849238,0.000734407,0.001009114,0.0002368811,0.0005782634,0.0003589106,0.0004554524,0.0007025064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002546214,"about_ca_system_score_gemma":0.0007511174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002892627,"about_ca_topic_score_gemma":0.004190147,"domain_scores_codex":[0.999762,0.00002928871,0.000017298,0.00006735654,0.00008451031,0.00003952983],"domain_scores_gemma":[0.9996963,0.0000740879,0.0000413158,0.00003730045,0.0001343227,0.00001668144],"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":[0.000533633,0.0002257491,0.004380909,0.0001429982,0.0001115182,0.0001934295,0.0001057236,0.02263759,0.2514342,0.001717841,0.002017084,0.7164992],"study_design_scores_gemma":[0.00001934708,0.0001852587,0.009969576,0.00002065465,0.00012648,0.0004660041,0.00006139196,0.8535128,0.1317572,0.00109403,0.002749229,0.00003802481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07967883,0.0003149449,0.9175636,0.00007113124,0.00003163288,0.00006341499,0.0001022761,0.001326027,0.0008481309],"genre_scores_gemma":[0.3775654,0.0003165034,0.618676,0.00006480008,0.00003576147,0.00008775263,0.0004577446,0.0001892691,0.002606712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002892627,"threshold_uncertainty_score":0.00575155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328594289936734,"score_gpt":0.2776363000420582,"score_spread":0.2543503571426908,"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."}}