{"id":"W4415289482","doi":"10.71335/z1yf5431","title":"<b>Deep Learning-Based Monkeypox Detection: A</b> <b>Hybrid Approach Using DenseNet121 and</b> <b>MobileNetV2</b><b> </b>","year":2025,"lang":"","type":"article","venue":"Midocean Journal for Research and Studies","topic":"Poxvirus research and outbreaks","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Monkeypox; Biometrics; Outbreak; Pattern recognition (psychology); Lesion; Skin lesion","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":[0.0005927936,0.00202661,0.0008495589,0.001258188,0.0004691182,0.001002412,0.001906891,0.001569304,0.004051599],"category_scores_gemma":[0.001253296,0.000529768,0.0009250701,0.0007196486,0.0003697789,0.001302591,0.001268195,0.001542267,0.002465355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009148167,"about_ca_system_score_gemma":0.00151121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02259109,"about_ca_topic_score_gemma":0.02838999,"domain_scores_codex":[0.9996369,0.00003873006,0.00001871492,0.0001401292,0.00007717716,0.00008830752],"domain_scores_gemma":[0.9996916,0.00005428173,0.00002373687,0.00006775096,0.0001209311,0.00004167835],"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":[0.0004070981,0.0006094112,0.008558657,0.0002673188,0.0003126343,0.0004048271,0.00009860393,0.1624468,0.02301632,0.002637076,0.05823956,0.7430018],"study_design_scores_gemma":[0.00002820075,0.00007560786,0.0009710501,0.00002416551,0.00002624756,0.00008120509,0.00003026826,0.9869894,0.006077244,0.001822793,0.003857733,0.00001601787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2062902,0.003563257,0.7211012,0.003183868,0.001106942,0.0007974264,0.007490314,0.03792851,0.01853831],"genre_scores_gemma":[0.6482491,0.0009771819,0.3040598,0.001678396,0.0002464415,0.0004788619,0.02057346,0.0008309268,0.02290587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02259109,"threshold_uncertainty_score":0.04491919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06102188188699372,"score_gpt":0.3473285035137329,"score_spread":0.2863066216267392,"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."}}