{"id":"https://openalex.org/W4362663462","doi":"https://doi.org/10.1117/12.2653792","title":"Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks","display_name":"Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks","publication_year":2023,"publication_date":"2023-04-06","ids":{"openalex":"https://openalex.org/W4362663462","doi":"https://doi.org/10.1117/12.2653792"},"language":"en","primary_location":{"id":"doi:10.1117/12.2653792","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653792","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110502345","display_name":"Ryo Toda","orcid":null},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryo Toda","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074920808","display_name":"Masahiro Oda","orcid":"https://orcid.org/0000-0001-7714-422X"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Oda","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088647628","display_name":"Yuichiro Hayashi","orcid":null},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuichiro Hayashi","raw_affiliation_strings":["Nagoya Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036871773","display_name":"Yoshito Otake","orcid":null},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]},{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshito Otake","raw_affiliation_strings":["Nara Institute of Science and Technology (Japan)","National Institute of Informatics (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nara Institute of Science and Technology (Japan)","institution_ids":["https://openalex.org/I75917431"]},{"raw_affiliation_string":"National Institute of Informatics (Japan)","institution_ids":["https://openalex.org/I184597095"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016809176","display_name":"Masahiro Hashimoto","orcid":"https://orcid.org/0000-0003-0162-5312"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Hashimoto","raw_affiliation_strings":["Keio Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keio Univ. (Japan)","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060878563","display_name":"Toshiaki Akashi","orcid":"https://orcid.org/0000-0002-3056-0792"},"institutions":[{"id":"https://openalex.org/I34077901","display_name":"Juntendo University","ror":"https://ror.org/01692sz90","country_code":"JP","type":"education","lineage":["https://openalex.org/I34077901"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshiaki Akashi","raw_affiliation_strings":["Juntendo Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Juntendo Univ. (Japan)","institution_ids":["https://openalex.org/I34077901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091368226","display_name":"Shigeki Aoki","orcid":"https://orcid.org/0000-0002-8491-0698"},"institutions":[{"id":"https://openalex.org/I34077901","display_name":"Juntendo University","ror":"https://ror.org/01692sz90","country_code":"JP","type":"education","lineage":["https://openalex.org/I34077901"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeki Aoki","raw_affiliation_strings":["Juntendo Univ. (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Juntendo Univ. (Japan)","institution_ids":["https://openalex.org/I34077901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032527419","display_name":"Kensaku Mori","orcid":"https://orcid.org/0000-0002-0100-4797"},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]},{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kensaku Mori","raw_affiliation_strings":["Nagoya Univ. (Japan)","National Institute of Informatics (Japan)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya Univ. (Japan)","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"National Institute of Informatics (Japan)","institution_ids":["https://openalex.org/I184597095"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2193,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55605159,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"12033","issue":null,"first_page":"11","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.744988203048706},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.7274137735366821},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7198865413665771},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.6731834411621094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6476365327835083},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"Computer-aided diagnosis","score":0.6059331893920898},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6030454635620117},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5629084706306458},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.54070645570755},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4535856544971466},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.44576749205589294},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41741132736206055},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41380447149276733},{"id":"https://openalex.org/keywords/cad","display_name":"CAD","score":0.41100674867630005},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4107819199562073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36342889070510864},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.2747099995613098},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19752562046051025},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14684203267097473},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.10565844178199768},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.09929478168487549}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.744988203048706},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.7274137735366821},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7198865413665771},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.6731834411621094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6476365327835083},{"id":"https://openalex.org/C2779549770","wikidata":"https://www.wikidata.org/wiki/Q1122413","display_name":"Computer-aided diagnosis","level":2,"score":0.6059331893920898},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6030454635620117},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5629084706306458},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.54070645570755},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4535856544971466},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.44576749205589294},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41741132736206055},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41380447149276733},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.41100674867630005},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4107819199562073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36342889070510864},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2747099995613098},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19752562046051025},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14684203267097473},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.10565844178199768},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.09929478168487549},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199639397","wikidata":"https://www.wikidata.org/wiki/Q1788588","display_name":"Engineering drawing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2653792","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653792","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2024931048","https://openalex.org/W3013377280","https://openalex.org/W3013732976","https://openalex.org/W3015141576","https://openalex.org/W3046722451","https://openalex.org/W3100327638","https://openalex.org/W3127784112","https://openalex.org/W3202131831","https://openalex.org/W4293200287","https://openalex.org/W6790849161","https://openalex.org/W6795660461","https://openalex.org/W6809739321"],"related_works":["https://openalex.org/W1589419489","https://openalex.org/W4229543669","https://openalex.org/W1783185948","https://openalex.org/W1554029525","https://openalex.org/W2999505641","https://openalex.org/W2401866201","https://openalex.org/W2945629533","https://openalex.org/W615772105","https://openalex.org/W2013631688","https://openalex.org/W1978794434"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"the":[3,7,56,80,105,111,133,149,175,179],"improved":[4],"method":[5,138],"for":[6,110],"COVID-19":[8,48,112],"classification":[9,113,167,180],"based":[10,114],"on":[11,115,169],"computed":[12],"tomography":[13],"(CT)":[14],"volumes":[15,155],"using":[16],"a":[17,20,53,108],"combination":[18,176],"of":[19,47,79,107,156],"complex-architecture":[21],"convolutional":[22],"neural":[23],"network":[24],"(CNN)":[25],"and":[26,44,55,73],"orthogonal":[27],"ensemble":[28,84,136],"networks":[29],"(OEN).":[30],"The":[31,119,166],"novel":[32],"coronavirus":[33],"disease":[34],"reported":[35],"in":[36,51,96,123,160,164],"2019":[37],"(COVID-19)":[38],"is":[39,49,59,83],"still":[40],"spreading":[41],"worldwide.":[42],"Early":[43],"accurate":[45],"diagnosis":[46,65],"required":[50],"such":[52],"situation,":[54],"CT":[57,117,129,154],"scan":[58],"an":[60,135],"essential":[61],"examination.":[62],"Various":[63],"computer-aided":[64],"(CAD)":[66],"methods":[67,82,87],"have":[68],"been":[69],"developed":[70],"to":[71,103,142],"assist":[72],"accelerate":[74],"doctors\u2019":[75],"diagnoses.":[76],"Although":[77],"one":[78],"effective":[81],"learning,":[85],"existing":[86],"combine":[88],"some":[89],"major":[90],"models":[91],"which":[92],"do":[93],"not":[94],"specialize":[95],"COVID-19.":[97],"In":[98],"this":[99],"study,":[100],"we":[101],"attempted":[102],"improve":[104,178],"performance":[106],"CNN":[109,120],"chest":[116,128,153],"volumes.":[118,130],"model":[121],"specializes":[122],"feature":[124,145],"extraction":[125,146],"from":[126],"anisotropic":[127],"We":[131,151],"adopt":[132],"OEN,":[134],"learning":[137],"considering":[139],"inter-model":[140],"diversity,":[141],"boost":[143],"its":[144],"ability.":[147],"For":[148],"experiment,":[150],"used":[152],"1283":[157],"cases":[158,172],"acquired":[159],"multiple":[161],"medical":[162],"institutions":[163],"Japan.":[165],"result":[168],"257":[170],"test":[171],"indicated":[173],"that":[174],"could":[177],"performance.":[181]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}