{"id":"W4205466533","doi":"10.18280/ts.380616","title":"Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Sørensen–Dice coefficient; Random forest; Computer science; Artificial intelligence; Stroke (engine); Segmentation; Convolutional neural network; Deep learning; Artificial neural network; Convolution (computer science); Pattern recognition (psychology); Image segmentation; Machine learning; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001763804,0.0001469598,0.000168372,0.00007767218,0.0002833614,0.00006756887,0.0001181634,0.00006169413,0.0002014986],"category_scores_gemma":[0.00006097851,0.000153121,0.0001150456,0.0004247925,0.00005842886,0.0002120123,0.00003795013,0.0002524612,0.00001119724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006719153,"about_ca_system_score_gemma":0.00003919022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006190261,"about_ca_topic_score_gemma":0.000008593857,"domain_scores_codex":[0.9983792,0.0002255982,0.0003525972,0.0003857744,0.0003692186,0.000287564],"domain_scores_gemma":[0.9993966,0.00008684627,0.0002055574,0.0001488591,0.0000805586,0.00008159272],"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":[0.00005172801,0.00005729488,0.0001614692,0.00001311949,0.000005047379,0.00001039131,0.0001880144,0.1518595,0.8385429,0.0003396146,0.000003120934,0.00876778],"study_design_scores_gemma":[0.0003061657,0.00006500898,0.000800839,0.000008334231,0.00001697047,0.00003155852,0.00004616359,0.5752371,0.423197,0.0001224063,0.0000771897,0.00009131262],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8749099,0.00004877313,0.1238923,0.00007612134,0.0002037088,0.0001266703,0.000002929057,0.00009042054,0.0006492427],"genre_scores_gemma":[0.9984304,0.000008228266,0.0007917176,0.0001929787,0.0001752367,0.00001304942,0.000002234009,0.0000220915,0.0003640746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4233776,"threshold_uncertainty_score":0.6244093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03844452189882566,"score_gpt":0.2465693932948275,"score_spread":0.2081248713960019,"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."}}