{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004356935,0.0003237098,0.0007644778,0.001495978,0.0002058403,0.0005111965,0.0003663501,0.0004226527,0.00118292],"category_scores_gemma":[0.0009185899,0.0001581936,0.0006183064,0.00167093,0.0002106835,0.000592752,0.0003232944,0.0003822877,0.0006748128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002987737,"about_ca_system_score_gemma":0.0005865647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001853774,"about_ca_topic_score_gemma":0.001422062,"domain_scores_codex":[0.9995803,0.00005610388,0.00003203973,0.00005982545,0.0002351532,0.00003665045],"domain_scores_gemma":[0.999745,0.00006365597,0.00003011642,0.00002778813,0.0001225975,0.00001082572],"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":[0.0001880953,0.0001075092,0.002136445,0.0002449952,0.00006520417,0.0001771532,0.00004998733,0.01521205,0.08386812,0.004494956,0.005664099,0.8877914],"study_design_scores_gemma":[0.00007052282,0.0005258461,0.02012694,0.00006672631,0.0001957552,0.001320874,0.0001127759,0.8956448,0.05812754,0.005788165,0.01791579,0.0001043036],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03207746,0.002049208,0.9628797,0.0001550417,0.0001407484,0.00009488885,0.0002497338,0.001009175,0.001344009],"genre_scores_gemma":[0.3673193,0.002154936,0.6255801,0.0001036894,0.0001653611,0.0002266235,0.001157993,0.00007785795,0.003214199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001853774,"threshold_uncertainty_score":0.003957212,"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."}}