{"id":"W4200055462","doi":"10.22266/ijies2022.0228.38","title":"Quad Convolutional Layers (QCL) CNN Approach for Classification of Brain Stroke in Diffusion Weighted (DW) - Magnetic Resonance Images (MRI)","year":2021,"lang":"en","type":"article","venue":"International journal of intelligent engineering and systems","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Lembaga Pengelola Dana Pendidikan; Kementerian Riset, Teknologi dan Pendidikan Tinggi; Universitas Airlangga","keywords":"Computer science; Magnetic resonance imaging; Diffusion MRI; Convolutional neural network; Diffusion-Weighted Magnetic Resonance Imaging; Diffusion; T2 weighted; Artificial intelligence; Nuclear magnetic resonance; Pattern recognition (psychology); Radiology; Medicine; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004289391,0.000114774,0.0002089216,0.0003003332,0.00002974644,0.00006608081,0.0002057395,0.00006740437,0.00001131001],"category_scores_gemma":[0.000688915,0.000108419,0.00009305406,0.0001695271,0.00004555807,0.0001371721,0.00002332006,0.0001709688,0.000001099075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422995,"about_ca_system_score_gemma":0.00006941106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001153846,"about_ca_topic_score_gemma":0.000001444808,"domain_scores_codex":[0.9984277,0.0000911941,0.0007016052,0.0002096857,0.0004435907,0.0001262229],"domain_scores_gemma":[0.9987113,0.0004056672,0.0003142828,0.0001000365,0.0004069807,0.00006167295],"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":[0.0001879932,0.0002122767,0.001456891,0.0001006482,0.00002136565,0.00001653366,0.0002477465,0.0076727,0.970529,0.01330482,0.0002845662,0.005965455],"study_design_scores_gemma":[0.001373706,0.0001715338,0.01647589,0.0003716208,0.00001386166,0.0005316162,0.001008427,0.787475,0.1804372,0.0001023403,0.01182766,0.0002111066],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5463811,0.004484342,0.4448494,0.001022188,0.002414196,0.000342489,0.0001051417,0.00003120368,0.0003698736],"genre_scores_gemma":[0.9976029,0.0004310619,0.001130435,0.00003375786,0.0001652323,0.00002017726,0.000009426215,0.00001316973,0.0005938821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7900918,"threshold_uncertainty_score":0.4421197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02949858886810632,"score_gpt":0.2614584006905025,"score_spread":0.2319598118223962,"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."}}