{"id":"W4283773504","doi":"10.3390/diagnostics12071607","title":"Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features","year":2022,"lang":"en","type":"article","venue":"Diagnostics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Prince Mohammad Bin Fahd University; Commonwealth Cyber Initiative","keywords":"Diabetic retinopathy; Artificial intelligence; Fundus (uterus); Computer science; Convolutional neural network; Pattern recognition (psychology); Retina; Feature (linguistics); Retinopathy; Feature extraction; Support vector machine; Computer vision; Ophthalmology; Medicine; Diabetes mellitus","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":[0.000276381,0.0004049906,0.0003938678,0.001524544,0.0001423968,0.000431949,0.0003347123,0.0003738555,0.0006643973],"category_scores_gemma":[0.0005606314,0.0001781824,0.0004419203,0.0005356388,0.00009856928,0.0003608638,0.0003668575,0.0003276895,0.0002248846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003377272,"about_ca_system_score_gemma":0.0002415817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003711059,"about_ca_topic_score_gemma":0.005025385,"domain_scores_codex":[0.9998243,0.00001963239,0.00001355215,0.00004248883,0.00006387253,0.00003609056],"domain_scores_gemma":[0.9997894,0.0000346566,0.00004110725,0.00002243586,0.0000962409,0.00001616332],"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":[0.0007450772,0.0004415509,0.02492892,0.0002262199,0.0002306559,0.0006243903,0.00009445619,0.02840105,0.202663,0.0004893463,0.003688672,0.7374668],"study_design_scores_gemma":[0.00002685698,0.0002682247,0.04048581,0.00004141127,0.0001130585,0.0008996936,0.0000682207,0.8737071,0.08237597,0.0006002475,0.001377004,0.00003657621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7005668,0.001851462,0.290466,0.0003163468,0.00008785298,0.0001150132,0.001006848,0.002241214,0.003348453],"genre_scores_gemma":[0.9273807,0.0005127923,0.0694363,0.0001003679,0.00003350416,0.00003249989,0.0008130186,0.00002608323,0.001664782],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003711059,"threshold_uncertainty_score":0.007378876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008561041427438455,"score_gpt":0.247544086546006,"score_spread":0.2389830451185675,"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."}}