{"id":"W4415289482","doi":"10.71335/z1yf5431","title":"&lt;b&gt;Deep Learning-Based Monkeypox Detection: A&lt;/b&gt; &lt;b&gt;Hybrid Approach Using DenseNet121 and&lt;/b&gt; &lt;b&gt;MobileNetV2&lt;/b&gt;&lt;b&gt; &lt;/b&gt;","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.02042638,0.005557209,0.008001498,0.007309773,0.02390828,0.003512803,0.005123203,0.003503961,0.001356449],"category_scores_gemma":[0.01140196,0.005230595,0.003409824,0.005842375,0.01253019,0.002344098,0.005590272,0.01135215,0.0006248682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003408157,"about_ca_system_score_gemma":0.004628668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000992663,"about_ca_topic_score_gemma":0.002298723,"domain_scores_codex":[0.9561057,0.01070088,0.007107858,0.007164542,0.004432459,0.01448857],"domain_scores_gemma":[0.9724464,0.008320916,0.00284117,0.003704897,0.008778961,0.003907593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.02700728,0.004543231,0.001718469,0.004962604,0.01889252,0.003252423,0.005034634,0.00241456,0.7157114,0.005212795,0.1266419,0.08460817],"study_design_scores_gemma":[0.030255,0.01476223,0.004182206,0.004013523,0.002581783,0.006457525,0.003746852,0.01689203,0.04429961,0.005142131,0.8608226,0.006844485],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4960987,0.4723625,0.006689073,0.003477364,0.005744619,0.00757484,0.001402254,0.0006381823,0.006012504],"genre_scores_gemma":[0.7993225,0.1585678,0.001563622,0.0005694176,0.003586408,0.001021391,0.0005534402,0.0008332235,0.03398217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7341807,"threshold_uncertainty_score":0.9995564,"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."}}