{"id":"W4289852144","doi":"10.1371/journal.pone.0269826","title":"A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Overfitting; Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Preprocessor; Pattern recognition (psychology); Skin cancer; Robustness (evolution); Machine learning; Contextual image classification; Feature engineering; Artificial neural network; Image (mathematics); Cancer; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003842858,0.0001445168,0.0003751827,0.0001948413,0.0002886822,0.00003493936,0.00007460835,0.00003533286,0.0007686365],"category_scores_gemma":[0.00006389963,0.0001535839,0.00004857877,0.0003486895,0.00001882621,0.00002235274,0.0003108699,0.0003239497,0.00002612184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002981119,"about_ca_system_score_gemma":0.00002646308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004016074,"about_ca_topic_score_gemma":0.00003054469,"domain_scores_codex":[0.9985297,0.0001655771,0.0001854746,0.0004053284,0.0004422851,0.000271599],"domain_scores_gemma":[0.9994172,0.00005456713,0.00005996574,0.0001806591,0.00004664103,0.0002409636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003413576,0.005454255,0.005773568,0.001216811,0.002558258,0.0002663991,0.01660973,0.07324236,0.828169,0.000643265,0.000994487,0.06165826],"study_design_scores_gemma":[0.002514795,0.0007654573,0.006145183,0.0002570007,0.001160954,0.0002654283,0.003813139,0.9539468,0.008070435,0.00004743884,0.02231549,0.0006979119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9296484,0.0001719665,0.05741962,0.002948107,0.00006613462,0.001317608,0.00001277451,0.0001896296,0.00822575],"genre_scores_gemma":[0.4066081,0.00001133184,0.5804867,0.002156555,0.0002401091,0.0002224875,0.0000189022,0.00006167325,0.01019413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8807044,"threshold_uncertainty_score":0.8416031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0953201161204921,"score_gpt":0.2980528350917569,"score_spread":0.2027327189712648,"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."}}