{"id":"W4316362920","doi":"10.18280/ts.390639","title":"Development of Medical Image Analytics by Deep Learning Model for Prediction and Classification of CT Image Diseases","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lung cancer; Convolutional neural network; Residual neural network; Adenocarcinoma; Artificial intelligence; Cancer; Computed tomography; Artificial neural network; Medicine; Deep learning; Lung; Disease; Computer science; Radiology; Pattern recognition (psychology); Pathology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004576013,0.0006155816,0.0005367602,0.0005950122,0.0001979459,0.0006058222,0.0008673482,0.0007976118,0.001499498],"category_scores_gemma":[0.001036565,0.0003210197,0.0007673951,0.0004438842,0.0001749641,0.0007683673,0.0004869799,0.001173408,0.0005928557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006920079,"about_ca_system_score_gemma":0.000989601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01329689,"about_ca_topic_score_gemma":0.01049021,"domain_scores_codex":[0.9998471,0.00002297186,0.00001250112,0.00004647822,0.0000441237,0.00002688073],"domain_scores_gemma":[0.9997482,0.00008112969,0.00002030644,0.00001773159,0.0001155515,0.00001715392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001622138,0.0002455913,0.0044651,0.0001057891,0.0001351838,0.0001571876,0.00004784342,0.673817,0.008533833,0.003398523,0.005728757,0.303203],"study_design_scores_gemma":[0.000001576056,0.00001075784,0.0001233099,0.0000028777,0.000003767139,0.000006452253,0.000001782548,0.9987011,0.0005623546,0.0004158634,0.0001684669,0.000001690248],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06270336,0.001780714,0.9273552,0.0009455782,0.0001704499,0.000133625,0.0006550841,0.003287422,0.002968587],"genre_scores_gemma":[0.7781833,0.001469873,0.2113861,0.0003872277,0.0001035827,0.0002851964,0.001830912,0.0001105445,0.006243326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01329689,"threshold_uncertainty_score":0.02643901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890351509754205,"score_gpt":0.2923518901071104,"score_spread":0.2734483750095684,"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."}}