{"id":"W4206132699","doi":"10.17762/de.vi.7730","title":"Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis","year":2021,"lang":"en","type":"article","venue":"Design Engineering","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Computer science; Gabor filter; Support vector machine; Kernel principal component analysis; Glaucoma; Classifier (UML); Dimensionality reduction; Feature extraction; Image processing; Computer vision; Image (mathematics); Kernel method; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001950267,0.0001702938,0.0004093341,0.0002420766,0.00006861365,0.00005692363,0.00004199189,0.00005796458,0.00002068644],"category_scores_gemma":[0.0001554772,0.0001560357,0.0002327936,0.0006152564,0.00001590982,0.00002241068,0.00003051506,0.0001211817,0.000001028829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007190086,"about_ca_system_score_gemma":0.00002999353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001415237,"about_ca_topic_score_gemma":9.796054e-7,"domain_scores_codex":[0.9990556,0.00002640911,0.000175903,0.0003073603,0.000170344,0.0002643683],"domain_scores_gemma":[0.9993222,0.0001460297,0.00003884504,0.0002149712,0.0001260282,0.0001518864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001384284,0.0002669929,0.2710413,0.0005434688,0.003512666,0.0003663442,0.0004622315,0.4301547,0.291886,0.00007501998,0.0004403633,0.001112426],"study_design_scores_gemma":[0.0005313904,0.00003332839,0.05582065,0.0001138596,0.002289556,0.00005407851,0.00003342194,0.9195635,0.02053444,0.000001013972,0.0008558895,0.0001689251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5650175,0.0007077426,0.4338402,0.0002199113,0.00004604363,0.0001155179,0.000007327384,0.00004147569,0.000004296459],"genre_scores_gemma":[0.8694006,0.00002579382,0.1301575,0.00008592315,0.000104248,0.00003882579,0.00002998895,0.00002610248,0.000131058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4894087,"threshold_uncertainty_score":0.636295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03580463270043457,"score_gpt":0.2633017950061698,"score_spread":0.2274971623057352,"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."}}