{"id":"W4382996379","doi":"10.1007/978-981-99-3416-4_30","title":"Detection and Identification of Lung Cancer Using an Improvised CNN Model: A Novel Approach to Assist Doctors in Diagnosing Lung Cancer","year":2023,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Lung cancer; Medicine; Boosting (machine learning); Lung; Artificial neural network; Radiology; Identification (biology); Lung cancer screening; Computer science; Artificial intelligence; Pathology; Internal medicine; Biology","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":[0.0004247226,0.0007074316,0.0003687439,0.0003912793,0.0001796857,0.0006029925,0.001117929,0.001004076,0.00223135],"category_scores_gemma":[0.0006778987,0.0002946991,0.0005881842,0.0003358161,0.000201095,0.0005688198,0.0004049858,0.0007687481,0.0009437617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005316761,"about_ca_system_score_gemma":0.0005526519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008060337,"about_ca_topic_score_gemma":0.01530995,"domain_scores_codex":[0.9998707,0.00001376583,0.00000541249,0.00004042648,0.00004155182,0.00002813811],"domain_scores_gemma":[0.9998109,0.0000677743,0.00001428738,0.0000249356,0.00006878804,0.00001333166],"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":[0.0003674941,0.0002604102,0.00552584,0.0001523082,0.0001989023,0.0004880407,0.000107201,0.08874347,0.06885684,0.003562838,0.01481292,0.8169237],"study_design_scores_gemma":[0.000007317475,0.00007282677,0.001556209,0.00001217746,0.00006296173,0.0001797536,0.00002092428,0.9755082,0.01830066,0.001442797,0.002823605,0.00001264316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1564931,0.003683792,0.8120176,0.001896358,0.0008387883,0.0002026833,0.001184348,0.004935376,0.018748],"genre_scores_gemma":[0.6301618,0.001818771,0.3258077,0.001015016,0.0002857753,0.00009799158,0.001885775,0.0002593573,0.03866789],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008060337,"threshold_uncertainty_score":0.01602685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06923657538256475,"score_gpt":0.3405533270673203,"score_spread":0.2713167516847556,"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."}}