{"id":"W2091232955","doi":"10.1109/ner.2013.6696231","title":"Automatic detection of Alzheimer disease in brain magnetic resonance images using fractal features","year":2013,"lang":"en","type":"article","venue":"","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Hurst exponent; Detrended fluctuation analysis; Pattern recognition (psychology); Support vector machine; Artificial intelligence; Preprocessor; Fractal; Computer science; Kernel (algebra); Segmentation; Fractal analysis; Feature extraction; Magnetic resonance imaging; Feature (linguistics); Fractal dimension; Scaling; Mathematics; Statistics; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000552721,0.000358966,0.0004055515,0.003107699,0.0001970373,0.0005474495,0.0003108881,0.0004683542,0.0005061518],"category_scores_gemma":[0.001841752,0.0001785828,0.0003788756,0.0008175117,0.0002743348,0.0005984882,0.000238836,0.0002567569,0.0002913228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001810252,"about_ca_system_score_gemma":0.0001977048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006403295,"about_ca_topic_score_gemma":0.0009367763,"domain_scores_codex":[0.9997811,0.000047373,0.00002269308,0.00005177524,0.00007835853,0.00001889338],"domain_scores_gemma":[0.9992849,0.0003139027,0.0001469469,0.00005191721,0.0001774288,0.0000248624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002343078,0.0001456302,0.02171617,0.0002983807,0.0001017863,0.0003944312,0.0002020364,0.01745958,0.1350422,0.003771074,0.002738351,0.8178962],"study_design_scores_gemma":[0.00007382069,0.0005272626,0.1568996,0.0000999256,0.0002198201,0.004163184,0.0002061184,0.7248884,0.09124424,0.01159311,0.009884397,0.0002000275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2281691,0.001785023,0.7658055,0.0002671701,0.00009210318,0.0001077056,0.0004968018,0.001328033,0.001948503],"genre_scores_gemma":[0.6234811,0.0008900399,0.3740659,0.00005055555,0.0001216611,0.0000616918,0.0004697574,0.00004101546,0.0008183089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003107699,"threshold_uncertainty_score":0.002923071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01790028269734367,"score_gpt":0.2162086503900738,"score_spread":0.1983083676927301,"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."}}