{"id":"W4386830609","doi":"10.18280/ria.370425","title":"Diabetic Retinopathy Severity Categorization in Retinal Images Using Convolution Neural Network","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Categorization; Retinal; Convolutional neural network; Convolution (computer science); Computer science; Diabetic retinopathy; Artificial intelligence; Retinopathy; Pattern recognition (psychology); Artificial neural network; Ophthalmology; Optometry; Medicine; Diabetes mellitus; Endocrinology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003010325,0.000470322,0.0003808257,0.0009425958,0.0001820803,0.0005089862,0.0005155236,0.0005055667,0.0007015467],"category_scores_gemma":[0.0006232353,0.0001694662,0.0005773052,0.0005008731,0.0001578602,0.0003970464,0.0003035615,0.0004031699,0.0001836605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006734267,"about_ca_system_score_gemma":0.0003790787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01216369,"about_ca_topic_score_gemma":0.008830499,"domain_scores_codex":[0.9998443,0.00001698334,0.00001125635,0.00004763436,0.00004603686,0.00003380402],"domain_scores_gemma":[0.9998741,0.00002582595,0.00002283612,0.00001103896,0.00005600788,0.00001013225],"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":[0.000727165,0.0004340679,0.01956742,0.000151689,0.0002201604,0.0004182632,0.0001157296,0.339586,0.03911725,0.001790064,0.004234443,0.5936378],"study_design_scores_gemma":[0.000003268649,0.00005292485,0.003499659,0.000009087663,0.00001870388,0.00006175386,0.00001154766,0.9923563,0.003390752,0.0003401068,0.0002483486,0.000007571723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6215833,0.002176747,0.3678291,0.0006254351,0.0002310513,0.0001625408,0.0006398027,0.001763658,0.004988346],"genre_scores_gemma":[0.9532649,0.0005710628,0.04292761,0.0001068401,0.00003830705,0.0000399879,0.0005402209,0.0000194418,0.002491775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01216369,"threshold_uncertainty_score":0.02418578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0422947354637838,"score_gpt":0.3036563156568667,"score_spread":0.2613615801930829,"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."}}