{"id":"W4408032407","doi":"10.18280/ts.420120","title":"Brain Tumor Classification in MRI Using Hybrid ASA-Based Deep Learning and Masi-Entropy Multilayer Thresholding Segmentation with Sunflower Optimization","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":"Thresholding; Artificial intelligence; Sunflower; Segmentation; Deep learning; Pattern recognition (psychology); Entropy (arrow of time); Computer science; Biology; Physics; Agronomy","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":[0.0008688528,0.0007577513,0.0009797473,0.0007409588,0.0004175064,0.0008229578,0.000806869,0.001161137,0.001485527],"category_scores_gemma":[0.001067201,0.0004032627,0.0008803185,0.0004667671,0.0003638366,0.0008481923,0.0006876663,0.0007907798,0.0003309194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006286054,"about_ca_system_score_gemma":0.001163164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005317711,"about_ca_topic_score_gemma":0.00781298,"domain_scores_codex":[0.9998267,0.00002855659,0.00001236046,0.00005311609,0.00004337997,0.00003582782],"domain_scores_gemma":[0.9997215,0.000112445,0.00003063448,0.00002100991,0.0000899638,0.00002449385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005696653,0.0002520877,0.00412422,0.0001329812,0.0001666839,0.0001290505,0.00009370694,0.5177695,0.02938811,0.005096428,0.003766176,0.4385115],"study_design_scores_gemma":[0.000003354232,0.00001859877,0.0002297379,0.00000337667,0.000007386891,0.0000120861,0.000003005645,0.9971611,0.001767577,0.000673328,0.0001174534,0.000002881894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09818085,0.000538571,0.8976092,0.0003413104,0.00007086547,0.00005605225,0.0001789651,0.001585414,0.001438716],"genre_scores_gemma":[0.6593489,0.0002809236,0.3348747,0.0002346116,0.00007662853,0.0001149617,0.0005824565,0.0002255439,0.004261211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005317711,"threshold_uncertainty_score":0.01057351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02628689506241658,"score_gpt":0.2764630124963259,"score_spread":0.2501761174339093,"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."}}