{"id":"W4295035338","doi":"10.1109/nss/mic44867.2021.9875540","title":"An Efficient End-to-end Convolutional Neural Network for Classification of Diabetic Retinopathy using ResNet","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Adaptive histogram equalization; Convolutional neural network; Computer science; Artificial intelligence; Diabetic retinopathy; Pattern recognition (psychology); Feature extraction; Fundus (uterus); Confusion matrix; Retinopathy; Computer vision; Histogram; Medicine; Diabetes mellitus; Ophthalmology; Histogram equalization; Image (mathematics)","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":[0.001591659,0.0002332668,0.0005051949,0.0002300989,0.0004425444,0.0001991215,0.0003565884,0.00008596207,0.000302882],"category_scores_gemma":[0.0006740175,0.0002115099,0.0001299412,0.0009715632,0.001666411,0.000214081,0.0001188095,0.0003171869,0.000009510889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001019124,"about_ca_system_score_gemma":0.00121486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001119679,"about_ca_topic_score_gemma":0.000006298169,"domain_scores_codex":[0.9965267,0.00009759646,0.0005494747,0.0009011896,0.001236522,0.0006885351],"domain_scores_gemma":[0.9973275,0.0001587112,0.0001875332,0.000496695,0.0009562316,0.0008733017],"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":[0.00008338257,0.0002062864,0.01192585,0.00009698978,0.00002382469,0.0000477365,0.0005762546,0.0008540484,0.9753572,0.001218129,0.0003238898,0.009286399],"study_design_scores_gemma":[0.0006786581,0.0001491154,0.01707255,0.0005069827,0.0002035661,0.0001941551,0.0007768723,0.9733047,0.006395929,0.00006592256,0.0004107072,0.0002408238],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821891,0.0001249529,0.006616595,0.009668758,0.0006504489,0.0002520915,0.00002172868,0.00004600717,0.0004303186],"genre_scores_gemma":[0.9910533,0.00006252166,0.007195238,0.001120634,0.0004014021,0.000008724716,0.00002793794,0.00002681987,0.000103438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9724507,"threshold_uncertainty_score":0.8625124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0240486310348164,"score_gpt":0.307814469353084,"score_spread":0.2837658383182676,"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."}}