{"id":"W4401228055","doi":"10.1007/s00521-024-10225-y","title":"SkinNet-14: a deep learning framework for accurate skin cancer classification using low-resolution dermoscopy images with optimized training time","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Charles Darwin University","keywords":"Computational Science and Engineering; Computer science; Artificial intelligence; Skin cancer; Training (meteorology); Deep learning; Pattern recognition (psychology); Resolution (logic); Computer vision; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007397262,0.000951367,0.0006154153,0.0008093516,0.0002329754,0.0006207644,0.001467458,0.0008080093,0.002837544],"category_scores_gemma":[0.001270152,0.0004510315,0.0006254245,0.0004797493,0.0003503987,0.0008224702,0.0009083914,0.001063641,0.00102759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009551274,"about_ca_system_score_gemma":0.001334079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008271195,"about_ca_topic_score_gemma":0.01336946,"domain_scores_codex":[0.9997945,0.00003409371,0.000009037762,0.00006971673,0.00006041034,0.00003212925],"domain_scores_gemma":[0.9998005,0.00005326692,0.00002470864,0.00002705612,0.00007240357,0.00002217417],"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":[0.0004432589,0.0003811599,0.004367699,0.0002113944,0.0001988903,0.0002107761,0.00006253627,0.485788,0.02867248,0.004832249,0.02003674,0.4547949],"study_design_scores_gemma":[0.000008981677,0.00003350073,0.0002870076,0.000007310176,0.000007692948,0.00003113384,0.000004921154,0.9936585,0.003901668,0.001035265,0.001018032,0.000005929176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06223726,0.00112872,0.9224404,0.0004136515,0.0001590536,0.0001747738,0.001103763,0.009555514,0.002786786],"genre_scores_gemma":[0.5628902,0.0008557427,0.4214517,0.0005117361,0.00009846974,0.000309272,0.004152026,0.000523518,0.009207311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008271195,"threshold_uncertainty_score":0.01644611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03257895844605113,"score_gpt":0.3314168384903811,"score_spread":0.29883788004433,"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."}}