{"id":"W4389265315","doi":"10.1007/s12021-023-09646-2","title":"A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images","year":2023,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Eisai; Servier; Iran University of Medical Sciences; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; National Institute on Aging; Alzheimer's Association","keywords":"Computer science; Generalizability theory; Ensemble learning; Artificial intelligence; Preprocessor; Ensemble forecasting; Pattern recognition (psychology); Machine learning; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001641119,0.001097898,0.001326963,0.001579898,0.0005109064,0.0006137353,0.001005268,0.0008867182,0.0007063234],"category_scores_gemma":[0.002125563,0.0003458361,0.001432514,0.0009245071,0.0001917073,0.00101487,0.000790717,0.001224652,0.0003539806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005154789,"about_ca_system_score_gemma":0.0007981836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008546952,"about_ca_topic_score_gemma":0.01002384,"domain_scores_codex":[0.999423,0.000104819,0.00004187509,0.0001698024,0.0001618738,0.0000986576],"domain_scores_gemma":[0.9992787,0.0001931984,0.00006283622,0.00008879922,0.00032138,0.00005508004],"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":[0.0003375588,0.000242432,0.01575471,0.00008064627,0.0004716707,0.0002699335,0.0001488464,0.187402,0.0122912,0.001283383,0.004879691,0.776838],"study_design_scores_gemma":[0.000007431773,0.00008560876,0.002347652,0.0000158874,0.0001117532,0.0001193043,0.00002245912,0.9924865,0.00287688,0.001125353,0.0007848372,0.00001629191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1361879,0.003002084,0.8556727,0.0004692485,0.000260082,0.00009425548,0.000352595,0.001698062,0.002263151],"genre_scores_gemma":[0.838903,0.00131077,0.1549505,0.0002921805,0.0002041443,0.0001017088,0.001042916,0.00008716781,0.003107631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008546952,"threshold_uncertainty_score":0.01699442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0795164950424835,"score_gpt":0.334383714130416,"score_spread":0.2548672190879325,"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."}}