{"id":"W3103519842","doi":"10.22215/etd/2018-12657","title":"A CNN Based Method for Brain Tumor Detection","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial intelligence; Preprocessor; Computer science; Convolutional neural network; Segmentation; Pattern recognition (psychology); Pixel; Feature extraction; Image segmentation; Computer vision; Noise (video); Artificial neural network; Feature (linguistics); Convolution (computer science); Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005139767,0.0002839275,0.0002477408,0.0003124464,0.0003499335,0.0001207168,0.0002506754,0.0002343003,0.0007505189],"category_scores_gemma":[0.002333833,0.0002729394,0.0002269352,0.0004332256,0.00003463972,0.0001135974,0.000006396125,0.0001699599,0.000261612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001066179,"about_ca_system_score_gemma":0.0001485037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002292599,"about_ca_topic_score_gemma":0.000446431,"domain_scores_codex":[0.9979674,0.0002252109,0.0003709923,0.0008405101,0.0003099983,0.0002859347],"domain_scores_gemma":[0.9982697,0.0007244509,0.0003581289,0.000385303,0.0001664095,0.00009598097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003252265,0.00005285936,2.733286e-7,0.0001204942,0.000003759951,0.000001010574,0.00008093737,0.000001912879,0.9370654,0.001001991,0.005324604,0.05602156],"study_design_scores_gemma":[0.0004583163,0.0002097767,0.0001127992,0.00002511829,0.00002607177,0.000008124612,0.0001826211,0.03747701,0.8599886,0.0009998837,0.1002267,0.000284961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01897619,0.00001115768,0.9113855,0.001348101,0.005835402,0.002677851,0.00006734703,0.00108735,0.05861108],"genre_scores_gemma":[0.5955217,0.00000462498,0.05395201,0.01716497,0.001694199,0.003476633,0.000575943,0.0003815101,0.3272285],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8574335,"threshold_uncertainty_score":0.9999723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04268657023506837,"score_gpt":0.3439128756425778,"score_spread":0.3012263054075094,"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."}}