{"id":"W4391524272","doi":"10.26438/ijsrcse/v10i5.2736","title":"Brain Tumor Detection using Cellular Automata based image Segmentation techniques","year":2022,"lang":"en","type":"article","venue":"International Journal of Scientific Research in Computer Sciences and Engineering","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Université Laval; Ankara Universitesi","keywords":"Artificial intelligence; Segmentation; Computer science; Cellular automaton; Image (mathematics); Computer vision; Image segmentation; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.004745069,0.00006540459,0.00007691477,0.001552967,0.0004656131,0.0007064799,0.0005938942,0.00001242769,0.00002401923],"category_scores_gemma":[0.0001739262,0.00006347473,0.00003640999,0.001188645,0.0002420129,0.0006092728,0.0001967881,0.0003354222,0.000001089928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002699676,"about_ca_system_score_gemma":0.000121105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001273677,"about_ca_topic_score_gemma":0.000001247575,"domain_scores_codex":[0.9975799,0.0002192727,0.0003061522,0.0002777844,0.001414183,0.0002027101],"domain_scores_gemma":[0.9992703,0.0002773132,0.0001349938,0.0000847768,0.0001686242,0.00006400751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001493135,0.00004264987,0.00005621364,0.000006629442,0.000001328492,0.00005469279,0.00009854373,0.02597229,0.9535102,0.00009786247,0.00003806068,0.02010658],"study_design_scores_gemma":[0.0001740134,0.0001042003,0.0002209618,0.00002409623,4.909122e-7,0.0001817957,0.00005653585,0.6533961,0.3447393,0.0001166669,0.0009367341,0.00004911286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8515357,0.00001457338,0.145366,0.001099153,0.001828228,0.000103797,0.000004556209,0.00002422226,0.00002377006],"genre_scores_gemma":[0.9860967,0.000002531007,0.01368939,0.00007967003,0.0001102907,0.000008056261,6.398892e-7,0.000004568884,0.000008107529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6274238,"threshold_uncertainty_score":0.6812602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0884982656385699,"score_gpt":0.3592374257141672,"score_spread":0.2707391600755973,"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."}}