{"id":"W4402306899","doi":"10.18280/ts.410429","title":"Retinal Image Enhancement Through Hyperparameter Selection Using RSO for CLAHE to Classify Diabetic Retinopathy","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperparameter; Diabetic retinopathy; Adaptive histogram equalization; Selection (genetic algorithm); Computer science; Artificial intelligence; Retinal; Pattern recognition (psychology); Ophthalmology; Image (mathematics); Medicine; Histogram; Histogram equalization; Diabetes mellitus","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":[0.0004093616,0.000713543,0.0007241478,0.000606184,0.0002810637,0.0006392514,0.0006935608,0.0006507337,0.0008694201],"category_scores_gemma":[0.0009711787,0.0002699739,0.0007526894,0.0003138956,0.0003670867,0.0004287302,0.000382429,0.000476559,0.0001982197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004400518,"about_ca_system_score_gemma":0.0005726031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005381474,"about_ca_topic_score_gemma":0.005211968,"domain_scores_codex":[0.9997966,0.00003624014,0.00001550216,0.00006604294,0.00005344089,0.00003229838],"domain_scores_gemma":[0.9998068,0.00006716635,0.00003694878,0.00001715725,0.00005703499,0.00001479933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001934573,0.0001407539,0.003906415,0.00008819081,0.000111342,0.0001918605,0.000126498,0.7883962,0.02102776,0.002100971,0.001480683,0.1822357],"study_design_scores_gemma":[0.000004731479,0.00003133468,0.0003178183,0.00000354963,0.00001291638,0.00002186514,0.000006203677,0.9979495,0.001250793,0.0002305745,0.0001657134,0.000004993031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1346061,0.001133488,0.8581913,0.0003161539,0.00008254655,0.0001302555,0.00004941269,0.001003374,0.004487306],"genre_scores_gemma":[0.9124904,0.0003033216,0.0839868,0.0001460977,0.00004136671,0.0001392698,0.00009694195,0.00004907132,0.002746683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005381474,"threshold_uncertainty_score":0.01070029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03614912443465586,"score_gpt":0.3325421939704948,"score_spread":0.296393069535839,"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."}}