{"id":"W2915472881","doi":"10.1080/02564602.2019.1576550","title":"Mobile Phone based ensemble classification of Deep Learned Feature for Medical Image Analysis","year":2019,"lang":"en","type":"article","venue":"IETE Technical Review","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Pooling; Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Feature selection; Feature extraction; Medical diagnosis; Classifier (UML); Normalization (sociology); Deep learning; Feature (linguistics); Ensemble learning; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004822499,0.0005373878,0.0004464673,0.0007841297,0.0001097477,0.0003577871,0.0005290635,0.0005596173,0.002029879],"category_scores_gemma":[0.0009473861,0.0001624889,0.0004761288,0.000524989,0.00009135756,0.0005028093,0.0003940058,0.0004335445,0.001028468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003233067,"about_ca_system_score_gemma":0.0002414206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001431074,"about_ca_topic_score_gemma":0.002790095,"domain_scores_codex":[0.999774,0.0000284436,0.00001232783,0.00006835072,0.00008837483,0.00002845222],"domain_scores_gemma":[0.9996632,0.00009973491,0.00002220904,0.00003875414,0.0001608519,0.0000152954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002638363,0.0001284594,0.004235034,0.0001123899,0.0001114283,0.0001647257,0.00004513595,0.03593073,0.03915593,0.001154693,0.004552077,0.9141457],"study_design_scores_gemma":[0.00001313011,0.0002686928,0.004715596,0.00002502145,0.00009938507,0.0003344249,0.00003029143,0.934399,0.05190067,0.001228351,0.006959456,0.00002600209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09962682,0.00179707,0.889926,0.0002705723,0.0002303432,0.0001153903,0.0005324527,0.003347898,0.004153379],"genre_scores_gemma":[0.7216997,0.0009851645,0.2657531,0.0002233942,0.0001106734,0.0001267999,0.001266976,0.00008995309,0.009744233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002029879,"threshold_uncertainty_score":0.006790578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03270225887286166,"score_gpt":0.3843687257886274,"score_spread":0.3516664669157657,"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."}}