{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007725937,0.0003053497,0.0003974034,0.0003346758,0.001394834,0.0001008972,0.00008323029,0.0001538746,0.00003572054],"category_scores_gemma":[0.0001521719,0.0003014722,0.0001048858,0.0005288286,0.00008156957,0.0001195599,0.00005465767,0.0006923268,0.00001780469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000702646,"about_ca_system_score_gemma":0.00008268699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002489687,"about_ca_topic_score_gemma":0.00006469974,"domain_scores_codex":[0.9969305,0.0006516729,0.000496515,0.000635005,0.000779784,0.0005064782],"domain_scores_gemma":[0.9989152,0.0002735076,0.0002203538,0.000243415,0.0001553138,0.0001922336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005675211,0.0003265177,0.007259251,0.0003906877,0.000314421,0.0001640544,0.003981789,0.7041309,0.271943,0.0005358625,0.002598022,0.007787948],"study_design_scores_gemma":[0.001915701,0.001199979,0.003509416,0.0001301154,0.0001011844,0.0009576981,0.009969233,0.7266113,0.0006179156,0.000003273871,0.2548907,0.00009349659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7727826,0.003292286,0.2175421,0.00006648553,0.00113691,0.001370819,0.000002556958,0.0004623189,0.003343937],"genre_scores_gemma":[0.9944748,0.0000730416,0.0003552006,0.0001117139,0.0007199018,0.0002823041,0.0001047471,0.00007734251,0.003800899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2713251,"threshold_uncertainty_score":0.9999437,"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."}}