{"id":"W4220733064","doi":"10.18280/ria.360114","title":"Brain Tumor Classification Based on Enhanced CNN Model","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Segmentation; Brain tumor; Pattern recognition (psychology); Context (archaeology); Benchmark (surveying); Convolutional neural network; Contextual image classification; Process (computing); Deep learning; Machine learning; Artificial neural network; Image (mathematics); Pathology","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":[0.0002557373,0.0006556848,0.0003899671,0.000704055,0.0001359996,0.0005453216,0.0007200749,0.0005269001,0.001820417],"category_scores_gemma":[0.0006276527,0.0001965114,0.000589502,0.0004428331,0.0001746994,0.0008253508,0.0003592281,0.0004738017,0.0005688363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007115788,"about_ca_system_score_gemma":0.0004854928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01013816,"about_ca_topic_score_gemma":0.01030513,"domain_scores_codex":[0.9998426,0.00001326325,0.000008154257,0.00004987614,0.00004932747,0.00003669918],"domain_scores_gemma":[0.9998389,0.00002358071,0.00002012632,0.00001816159,0.00009023913,0.000008917118],"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":[0.0003621682,0.0001621387,0.008377238,0.0001722056,0.0001834157,0.0004604016,0.00006507849,0.5186716,0.03973407,0.005896513,0.0094897,0.4164255],"study_design_scores_gemma":[0.000003747484,0.000031326,0.001316717,0.000007152427,0.00002223056,0.00009595523,0.000005400671,0.9923609,0.004197754,0.0009307504,0.001021962,0.00000611587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2426639,0.002972968,0.7329111,0.0008761588,0.000361844,0.00015851,0.001421532,0.002930357,0.0157037],"genre_scores_gemma":[0.9064983,0.001418919,0.07623133,0.000252533,0.0001169958,0.00009810567,0.001797992,0.00009909546,0.01348684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01013816,"threshold_uncertainty_score":0.02015829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08656262275295305,"score_gpt":0.2982000523940131,"score_spread":0.21163742964106,"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."}}