{"id":"https://openalex.org/W4318147843","doi":"https://doi.org/10.1109/bigdata55660.2022.10020948","title":"Improving the performance of disease detection from chest X-ray images using deep learning","display_name":"Improving the performance of disease detection from chest X-ray images using deep learning","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147843","doi":"https://doi.org/10.1109/bigdata55660.2022.10020948"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020948","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020948","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5103356827","display_name":"Duong Hong Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I1314466530","display_name":"Tokai University","ror":"https://ror.org/01p7qe739","country_code":"JP","type":"education","lineage":["https://openalex.org/I1314466530"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Duong Hong Nguyen","raw_affiliation_strings":["Tokai University,Graduate School of Information and Telecommunication Engineering,Tokyo,Japan","Graduate School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokai University,Graduate School of Information and Telecommunication Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I1314466530"]},{"raw_affiliation_string":"Graduate School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan","institution_ids":["https://openalex.org/I1314466530"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060627916","display_name":"Iwao Fujino","orcid":null},"institutions":[{"id":"https://openalex.org/I1314466530","display_name":"Tokai University","ror":"https://ror.org/01p7qe739","country_code":"JP","type":"education","lineage":["https://openalex.org/I1314466530"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Iwao Fujino","raw_affiliation_strings":["Tokai University,Graduate School of Information and Telecommunication Engineering,Tokyo,Japan","Graduate School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokai University,Graduate School of Information and Telecommunication Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I1314466530"]},{"raw_affiliation_string":"Graduate School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan","institution_ids":["https://openalex.org/I1314466530"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103356827"],"corresponding_institution_ids":["https://openalex.org/I1314466530"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"3118","last_page":"3123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9988999962806702,"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":0.9988999962806702,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9983999729156494,"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/T10862","display_name":"AI in cancer detection","score":0.9976000189781189,"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":0.711431086063385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.649787187576294},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6235109567642212},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.6154145002365112},{"id":"https://openalex.org/keywords/thorax","display_name":"Thorax (insect anatomy)","score":0.5306130051612854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49734094738960266},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4639638662338257},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39228227734565735},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38322511315345764},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3291769027709961},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.22580599784851074},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07541826367378235}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.711431086063385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.649787187576294},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6235109567642212},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.6154145002365112},{"id":"https://openalex.org/C97834683","wikidata":"https://www.wikidata.org/wiki/Q942508","display_name":"Thorax (insect anatomy)","level":2,"score":0.5306130051612854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49734094738960266},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4639638662338257},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39228227734565735},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38322511315345764},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3291769027709961},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.22580599784851074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07541826367378235},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020948","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020948","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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":17,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2064675550","https://openalex.org/W2108598243","https://openalex.org/W2119605622","https://openalex.org/W2151103935","https://openalex.org/W2158698691","https://openalex.org/W2163605009","https://openalex.org/W2559874490","https://openalex.org/W2611650229","https://openalex.org/W2963446712","https://openalex.org/W3101156210","https://openalex.org/W4295608163","https://openalex.org/W4300485340","https://openalex.org/W6631190155","https://openalex.org/W6684191040","https://openalex.org/W6745119210","https://openalex.org/W6746693533"],"related_works":["https://openalex.org/W2527906156","https://openalex.org/W2385906609","https://openalex.org/W2368075107","https://openalex.org/W2391106160","https://openalex.org/W18538866","https://openalex.org/W4307092591","https://openalex.org/W4386178770","https://openalex.org/W4214662146","https://openalex.org/W4380075502","https://openalex.org/W2795259429"],"abstract_inverted_index":{"Early":[0],"diagnosis":[1],"of":[2,20,29,69,85],"thorax":[3],"diseases":[4,99],"is":[5,88],"very":[6],"important":[7],"for":[8,60],"saving":[9],"lives.":[10],"Recently,":[11],"deep":[12],"learning":[13],"approaches":[14],"are":[15,36],"applied":[16],"to":[17,51,57,73,93,96],"multilabel":[18],"classification":[19,75],"chest":[21,98],"X-ray":[22],"images,":[23],"which":[24],"yields":[25],"an":[26],"emerging":[27],"field":[28],"AI":[30],"diagnosis,":[31],"However,":[32],"so":[33],"far,":[34],"there":[35],"still":[37],"some":[38],"performance":[39],"problems":[40],"when":[41],"using":[42],"it":[43],"in":[44],"hospitals":[45],"and":[46,53,103],"clinics.":[47],"This":[48],"study":[49,87],"aims":[50],"compare":[52],"analyze":[54],"different":[55,71],"ways":[56],"balance":[58],"data":[59],"image":[61],"classification,":[62],"as":[63,65],"well":[64],"propose":[66],"a":[67,78],"method":[68],"combining":[70],"models":[72],"improve":[74],"performance.":[76],"Furthermore,":[77],"web":[79],"diagnostic":[80],"system":[81],"introduced":[82],"the":[83],"results":[84],"this":[86],"implemented.":[89],"It":[90],"will":[91],"contribute":[92],"helping":[94],"doctors":[95],"detect":[97],"more":[100],"accurately,":[101],"easily,":[102],"quickly.":[104]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}