{"id":"W4210346992","doi":"10.1111/exsy.12944","title":"<scp>SLDCNet</scp>: Skin lesion detection and classification using full resolution convolutional network‐based deep learning <scp>CNN</scp> with transfer learning","year":2022,"lang":"en","type":"article","venue":"Expert Systems","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Convolutional neural network; Skin lesion; Deep learning; Preprocessor; Pattern recognition (psychology); Segmentation; Skin cancer; Cancer; Medicine; Dermatology","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":[0.0002664316,0.0005477103,0.0002017449,0.0004465177,0.0001799762,0.0003738826,0.0008500415,0.0005639596,0.005165157],"category_scores_gemma":[0.0005843535,0.0001888009,0.0003362555,0.0003137507,0.0002339151,0.0005621256,0.0004288817,0.0005411748,0.001218297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006460641,"about_ca_system_score_gemma":0.0007859153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01664139,"about_ca_topic_score_gemma":0.01795282,"domain_scores_codex":[0.9998678,0.0000124167,0.000005790961,0.00003407236,0.00005604503,0.00002383202],"domain_scores_gemma":[0.9998264,0.00002679078,0.00001429548,0.00003041238,0.00008593859,0.00001621216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002463146,0.0002435271,0.003804375,0.0001941912,0.0001035224,0.0003597296,0.00004223186,0.3278197,0.03341816,0.003579659,0.04128159,0.5889071],"study_design_scores_gemma":[0.000007090169,0.00004126155,0.0006052464,0.000006645179,0.0000085352,0.00005381418,0.000005217827,0.986785,0.009601571,0.0005818392,0.002297226,0.000006608714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1340671,0.001679374,0.8175117,0.001535955,0.0004771927,0.00032554,0.002097821,0.01795314,0.02435218],"genre_scores_gemma":[0.8017915,0.0007266154,0.1705223,0.0005716589,0.0001040456,0.0001787687,0.004086117,0.0002948693,0.02172412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01664139,"threshold_uncertainty_score":0.03308904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02816095818177413,"score_gpt":0.2477237298285354,"score_spread":0.2195627716467613,"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."}}