{"id":"W4415170028","doi":"10.6000/1929-6029.2025.14.58","title":"AI-Powered CNN Model for Automated Lung Cancer Diagnosis in Medical Imaging","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprocessor; Convolutional neural network; Normalization (sociology); Medical imaging; Pixel; Grayscale; Deep learning; Pattern recognition (psychology)","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":[0.0005542382,0.0009339895,0.0005213758,0.00068363,0.0002633626,0.0006104215,0.001551386,0.001017988,0.003114637],"category_scores_gemma":[0.001373956,0.0003091531,0.0007361977,0.00060426,0.0002209122,0.0006895806,0.000547184,0.0009761378,0.001510128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276965,"about_ca_system_score_gemma":0.001247342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02164742,"about_ca_topic_score_gemma":0.02409016,"domain_scores_codex":[0.9997894,0.00003034906,0.00001287538,0.00006679438,0.00005416613,0.00004642052],"domain_scores_gemma":[0.9997515,0.00007299354,0.00002475394,0.00002695808,0.0001069073,0.0000168545],"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":[0.0006149316,0.0002835416,0.007816942,0.0003449944,0.0002570046,0.0003358883,0.00005805137,0.492422,0.01670273,0.003179867,0.0226625,0.4553215],"study_design_scores_gemma":[0.00000912154,0.00003999047,0.0009215039,0.00001681863,0.00002941662,0.00005266383,0.000005646887,0.9942468,0.002703996,0.0006702568,0.001296915,0.00000683005],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2372478,0.01083211,0.7105684,0.003159952,0.0009091735,0.0005079512,0.005487857,0.01229279,0.01899386],"genre_scores_gemma":[0.8634076,0.001953442,0.1099524,0.00087154,0.0002601899,0.0003134888,0.006921005,0.0001800117,0.01614046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02164742,"threshold_uncertainty_score":0.04304284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03140246757287672,"score_gpt":0.5195471447686191,"score_spread":0.4881446771957423,"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."}}