{"id":"W4401841081","doi":"10.1016/j.heliyon.2024.e36759","title":"Cataract and glaucoma detection based on Transfer Learning using MobileNet","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"King Saud University; Virtual University of Pakistan","keywords":"Glaucoma; Transfer of learning; Deep learning; Blindness; Computer science; Artificial intelligence; Cataracts; Optic nerve; Deep neural networks; Optometry; Machine learning; Ophthalmology; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001173467,0.00007780135,0.000114242,0.000147228,0.00006122242,0.00004217269,0.0000120424,0.00003562961,0.00004312515],"category_scores_gemma":[0.00002022897,0.00006288652,0.00006276293,0.0001660716,0.00001882082,0.00004004888,0.000002726878,0.0002043323,0.00002273686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003284947,"about_ca_system_score_gemma":0.00002050269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005911263,"about_ca_topic_score_gemma":0.000005336716,"domain_scores_codex":[0.9994791,0.00003225605,0.00008755722,0.0001796991,0.000118284,0.0001031584],"domain_scores_gemma":[0.9998075,0.00004111847,0.000005852327,0.00007546811,0.00001719239,0.00005288157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003161131,0.00009546586,0.0448507,0.002202663,0.0001166155,0.000354391,0.0004292354,0.00139238,0.6282688,0.00001607066,0.00001940938,0.3219381],"study_design_scores_gemma":[0.0006580352,0.0006572393,0.03597192,0.003051201,0.0005831973,0.0001799774,0.000163739,0.8555732,0.08784516,0.000007865136,0.01508221,0.0002263041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892254,0.002444222,0.007405416,0.0002898524,0.00006072702,0.00005393492,0.000001005395,0.00009780135,0.000421625],"genre_scores_gemma":[0.999013,0.0002401899,0.00012742,0.0001652393,0.0001147583,0.000002665656,0.000008006701,0.00001635699,0.0003123528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8541808,"threshold_uncertainty_score":0.2564438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440181227261772,"score_gpt":0.2818516916210932,"score_spread":0.2674498793484755,"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."}}