{"id":"https://openalex.org/W3158908738","doi":"https://doi.org/10.1145/3412841.3442091","title":"Detection of COVID-19 in chest X-ray images using transfer learning with deep convolutional neural network","display_name":"Detection of COVID-19 in chest X-ray images using transfer learning with deep convolutional neural network","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3158908738","doi":"https://doi.org/10.1145/3412841.3442091","mag":"3158908738"},"language":"en","primary_location":{"id":"doi:10.1145/3412841.3442091","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3442091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5035341456","display_name":"Luis H. S. Vogado","orcid":"https://orcid.org/0000-0002-4690-3921"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luis Vogado","raw_affiliation_strings":["Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076020389","display_name":"Pablo de Abreu Vieira","orcid":"https://orcid.org/0000-0003-0287-4046"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Pablo Vieira","raw_affiliation_strings":["Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103634140","display_name":"Pedro Santos Neto","orcid":null},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Pedro Santos Neto","raw_affiliation_strings":["Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011504158","display_name":"Lucas Ara\u00fajo Lopes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lucas Lopes","raw_affiliation_strings":["Maida.Health, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Maida.Health, Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044846704","display_name":"Gleison Silva","orcid":null},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gleison Silva","raw_affiliation_strings":["Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Maida.Health, Brazil and Federal University of Piau\u00ed, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072240451","display_name":"Fl\u00e1vio H. D. Ara\u00fajo","orcid":"https://orcid.org/0000-0003-2824-2645"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Fl\u00e1vio Ara\u00fajo","raw_affiliation_strings":["Federal University of Piau\u00ed, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Piau\u00ed, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005236232","display_name":"Rodrigo Veras","orcid":"https://orcid.org/0000-0001-8180-4032"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrigo Veras","raw_affiliation_strings":["Federal University of Piau\u00ed, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Piau\u00ed, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"629","last_page":"636"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9772999882698059,"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":0.9746999740600586,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8562995195388794},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.835788369178772},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.8192780017852783},{"id":"https://openalex.org/keywords/radiography","display_name":"Radiography","score":0.7229212522506714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6583707332611084},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5877219438552856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5787944197654724},{"id":"https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score":0.49171382188796997},{"id":"https://openalex.org/keywords/computer-aided-diagnosis","display_name":"Computer-aided diagnosis","score":0.45666956901550293},{"id":"https://openalex.org/keywords/2019-20-coronavirus-outbreak","display_name":"2019-20 coronavirus outbreak","score":0.4312819540500641},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42144346237182617},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.42112818360328674},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3287859559059143},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.323414146900177},{"id":"https://openalex.org/keywords/medical-physics","display_name":"Medical physics","score":0.3223545253276825},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.1930442750453949}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8562995195388794},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.835788369178772},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.8192780017852783},{"id":"https://openalex.org/C36454342","wikidata":"https://www.wikidata.org/wiki/Q245341","display_name":"Radiography","level":2,"score":0.7229212522506714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6583707332611084},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5877219438552856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5787944197654724},{"id":"https://openalex.org/C3007834351","wikidata":"https://www.wikidata.org/wiki/Q82069695","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","level":5,"score":0.49171382188796997},{"id":"https://openalex.org/C2779549770","wikidata":"https://www.wikidata.org/wiki/Q1122413","display_name":"Computer-aided diagnosis","level":2,"score":0.45666956901550293},{"id":"https://openalex.org/C3006700255","wikidata":"https://www.wikidata.org/wiki/Q81068910","display_name":"2019-20 coronavirus outbreak","level":3,"score":0.4312819540500641},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42144346237182617},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.42112818360328674},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3287859559059143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.323414146900177},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.3223545253276825},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.1930442750453949},{"id":"https://openalex.org/C116675565","wikidata":"https://www.wikidata.org/wiki/Q3241045","display_name":"Outbreak","level":2,"score":0.0},{"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/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3412841.3442091","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3442091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8600000143051147,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2012875423","https://openalex.org/W2117539524","https://openalex.org/W2133059825","https://openalex.org/W2148489082","https://openalex.org/W2433065461","https://openalex.org/W2811374795","https://openalex.org/W2895124230","https://openalex.org/W2911964244","https://openalex.org/W2974888503","https://openalex.org/W3006643024","https://openalex.org/W3007866917","https://openalex.org/W3007964452","https://openalex.org/W3013277995","https://openalex.org/W3017141460","https://openalex.org/W3017855299","https://openalex.org/W3020203706","https://openalex.org/W3021622280","https://openalex.org/W3024801014","https://openalex.org/W3031759249","https://openalex.org/W3032017599","https://openalex.org/W3036552116","https://openalex.org/W3036638392","https://openalex.org/W3159001838","https://openalex.org/W6681822247"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3183901164","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W4206357785","https://openalex.org/W3192840557","https://openalex.org/W4281381188","https://openalex.org/W2951211570","https://openalex.org/W3167935049"],"abstract_inverted_index":{"Over":[0],"the":[1,17,20,34,38,44,93,105,139,150,177,180,187,193],"years,":[2],"Computer-Aided":[3],"Diagnosis":[4],"(CAD)":[5],"systems":[6,24],"have":[7],"been":[8],"proving":[9],"their":[10],"effectiveness":[11],"in":[12,43,65,76,86,112,149,164,183],"classifying":[13],"many":[14],"pathologies.":[15,102],"With":[16],"advent":[18],"of":[19,33,46,96,100,131,141,147,176,190],"COVID-19":[21,85,152,157],"pandemic,":[22],"new":[23],"were":[25],"developed":[26,104],"quickly.":[27],"The":[28,167],"chest":[29,87],"radiography":[30,88],"is":[31],"one":[32],"least":[35],"expensive":[36],"among":[37],"imaging":[39],"exams":[40],"that":[41],"assist":[42],"detection":[45,55],"COVID-19.":[47,142,195],"Despite":[48],"not":[49],"having":[50],"high":[51],"sensitivity":[52],"for":[53,83,156,192],"pattern":[54],"compared":[56,173],"to":[57,138,174],"other":[58,101,132],"tests":[59],"-":[60,68],"such":[61],"as":[62],"ground-glass":[63],"opacities":[64],"computed":[66],"tomography":[67],"this":[69,77,184],"test":[70],"helps":[71],"screen":[72],"infected":[73],"patients.":[74],"Therefore,":[75],"work,":[78],"we":[79,125],"propose":[80],"a":[81],"methodology":[82,106],"detecting":[84],"considering":[89],"three":[90,165],"possible":[91],"scenarios:":[92],"healthy,":[94,154],"presence":[95,99,140],"COVID-19,":[97],"and":[98,134,161],"We":[103,143],"by":[107],"evaluating":[108],"transfer":[109],"learning":[110],"techniques":[111],"five":[113],"well":[114],"know":[115],"pre-trained":[116],"Convolutional":[117],"Neural":[118],"Networks":[119],"(CNNs)":[120],"architectures.":[121],"For":[122],"training":[123],"CNNs,":[124],"used":[126,182],"1,932":[127],"healthy":[128],"images,":[129],"3,651":[130],"pathologies,":[133,160],"1,436":[135],"images":[136],"related":[137],"obtained":[144],"an":[145],"accuracy":[146],"94.36%":[148],"scenario":[151],"vs.":[153,158],"99.80%":[155],"others":[159],"95.01%":[162],"differentiating":[163],"classes.":[166],"results":[168],"are":[169],"considered":[170],"promising":[171],"when":[172],"state":[175],"art":[178],"since":[179],"database":[181],"work":[185],"has":[186],"largest":[188],"number":[189],"examples":[191],"class":[194]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}