{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003127947,0.0009289047,0.0005855109,0.001885418,0.0003205641,0.0005365305,0.0005967307,0.0006614837,0.001563192],"category_scores_gemma":[0.000775575,0.0002865558,0.0007312779,0.0006755115,0.0002890581,0.0006552447,0.0005502976,0.0006205548,0.0005925359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000818162,"about_ca_system_score_gemma":0.0005665548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01408003,"about_ca_topic_score_gemma":0.01341246,"domain_scores_codex":[0.9998236,0.00002138979,0.000009355083,0.00005447882,0.00004114074,0.00005013007],"domain_scores_gemma":[0.9998112,0.00005087607,0.00002365535,0.00001959723,0.00007393999,0.00002078195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009806844,0.0006211175,0.02860121,0.0001579109,0.0003466601,0.001224981,0.0001115075,0.196312,0.02006719,0.002847077,0.01377474,0.734955],"study_design_scores_gemma":[0.00001515169,0.00009952414,0.003084038,0.00001639847,0.00003078196,0.0001761936,0.00002352035,0.9898255,0.004559102,0.001218258,0.0009366265,0.00001484826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.696645,0.004596123,0.2740265,0.001603903,0.0007110172,0.0002383603,0.001903179,0.006992791,0.01328315],"genre_scores_gemma":[0.9706852,0.0005385286,0.02258092,0.0002152454,0.0001148016,0.00004254559,0.001124957,0.00004337952,0.004654429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01408003,"threshold_uncertainty_score":0.02799618,"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."}}