{"id":"https://openalex.org/W4212809275","doi":"https://doi.org/10.1117/12.2613177","title":"A deep learning approach for COVID-19 screening and localization on chest x-ray images","display_name":"A deep learning approach for COVID-19 screening and localization on chest x-ray images","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4212809275","doi":"https://doi.org/10.1117/12.2613177"},"language":"en","primary_location":{"id":"doi:10.1117/12.2613177","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2613177","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","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":"Medical Imaging 2022: Computer-Aided Diagnosis","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/A5037665595","display_name":"Karem D. Marcomini","orcid":"https://orcid.org/0000-0002-8594-4654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karem D. Marcomini","raw_affiliation_strings":["Escola de Engenharia de S\u00e3o Carlos (Brazil)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Escola de Engenharia de S\u00e3o Carlos (Brazil)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101646759","display_name":"Diego Armando Cardona C\u00e1rdenas","orcid":"https://orcid.org/0000-0002-8846-5202"},"institutions":[{"id":"https://openalex.org/I4210140911","display_name":"Hospital do Cora\u00e7\u00e3o","ror":"https://ror.org/04dzaw261","country_code":"BR","type":"healthcare","lineage":["https://openalex.org/I4210140911"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Diego A. C. Cardenas","raw_affiliation_strings":["Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)","institution_ids":["https://openalex.org/I4210140911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014783020","display_name":"Agma J. M. Traina","orcid":"https://orcid.org/0000-0003-4929-7258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agma J. M. Traina","raw_affiliation_strings":["Univ. de S\u00e3o Paulo (Brazil)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. de S\u00e3o Paulo (Brazil)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088604083","display_name":"Jos\u00e9 Eduardo Krieger","orcid":"https://orcid.org/0000-0001-5464-1792"},"institutions":[{"id":"https://openalex.org/I4210140911","display_name":"Hospital do Cora\u00e7\u00e3o","ror":"https://ror.org/04dzaw261","country_code":"BR","type":"healthcare","lineage":["https://openalex.org/I4210140911"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jos\u00e9 E. Krieger","raw_affiliation_strings":["Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)","institution_ids":["https://openalex.org/I4210140911"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085369510","display_name":"Marco A. Guti\u00e9rrez","orcid":"https://orcid.org/0000-0003-0964-6222"},"institutions":[{"id":"https://openalex.org/I4210140911","display_name":"Hospital do Cora\u00e7\u00e3o","ror":"https://ror.org/04dzaw261","country_code":"BR","type":"healthcare","lineage":["https://openalex.org/I4210140911"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marco A. Gutierrez","raw_affiliation_strings":["Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)","institution_ids":["https://openalex.org/I4210140911"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01163354,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"103","last_page":"103"},"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":0.98580002784729,"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":0.980400025844574,"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/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.6776965260505676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5772793889045715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5420076251029968},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47932493686676025},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3692224621772766},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2091144323348999},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.07357668876647949}],"concepts":[{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.6776965260505676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5772793889045715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5420076251029968},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47932493686676025},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3692224621772766},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2091144323348999},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.07357668876647949},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2613177","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2613177","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","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":"Medical Imaging 2022: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2968605358","https://openalex.org/W3008985036","https://openalex.org/W3017855299","https://openalex.org/W3030621456","https://openalex.org/W3033814865","https://openalex.org/W3080902334","https://openalex.org/W3083972167","https://openalex.org/W3085360812","https://openalex.org/W3088680016","https://openalex.org/W3091787675","https://openalex.org/W3099183222","https://openalex.org/W3110602624","https://openalex.org/W3120506007","https://openalex.org/W3135057764","https://openalex.org/W4250170983","https://openalex.org/W4394654459","https://openalex.org/W6762718338"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W2058170566","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W3009238340","https://openalex.org/W2229312674","https://openalex.org/W2951359407"],"abstract_inverted_index":{"Chest":[0],"X-ray":[1],"(CXR)":[2],"images":[3,80,90],"have":[4],"a":[5,23,34,44,52,64,133,226,241],"high":[6],"potential":[7],"in":[8,59,136,194,205,247],"the":[9,20,79,93,98,111,119,140,151,157,166,197,221,231,239],"monitoring":[10],"and":[11,54,102,148,160,184,209,213,234],"examination":[12],"of":[13,22,26,78,176,186,211,228],"various":[14],"lung":[15,56],"diseases,":[16],"including":[17],"COVID-19.":[18],"However,":[19],"screening":[21],"large":[24],"number":[25],"patients":[27],"with":[28,105],"diagnostic":[29,236],"hypothesis":[30],"for":[31,37],"COVID-19":[32,82],"poses":[33],"major":[35],"challenge":[36],"physicians.":[38],"In":[39],"this":[40,129],"paper,":[41],"we":[42,114],"propose":[43],"deep":[45],"learning-based":[46],"approach":[47],"that":[48],"can":[49],"simultaneously":[50],"suggest":[51],"diagnose":[53],"localize":[55],"opacity":[57,222],"areas":[58],"CXR":[60,71],"images.":[61,72],"We":[62,96,131],"used":[63,126,132],"public":[65],"dataset":[66,99,138],"containing":[67],"5,":[68],"639":[69],"posteroanterior":[70],"Due":[73],"to":[74,89,92,118,127,139,156,165,244],"unbalanced":[75],"classes":[76],"(69.2%":[77],"are":[81],"positive),":[83],"data":[84],"augmentation":[85],"was":[86,125,203],"applied":[87,115],"only":[88],"belonging":[91],"normal":[94],"category.":[95],"split":[97],"into":[100],"train":[101],"test":[103,198],"sets":[104],"proportional":[106],"rate":[107],"at":[108],"90:10.":[109],"To":[110],"classification":[112,158,170],"task,":[113],"5-fold":[116,195],"cross-validation":[117],"training":[120],"set.":[121],"The":[122,169,200],"EfficientNetB4":[123],"architecture":[124],"perform":[128],"classification.":[130],"YOLOv5":[134],"pre-trained":[135],"COCO":[137],"detection":[141,167],"task.":[142,168],"Evaluations":[143],"were":[144,218],"based":[145],"on":[146],"accuracy":[147,175,212],"area":[149],"under":[150],"ROC":[152],"curve":[153],"(AUROC)":[154],"metrics":[155],"task":[159,171],"mean":[161],"average":[162,174],"precision":[163],"(mAP)":[164],"achieved":[172,225],"an":[173],"0.83":[177],"&plusmn;":[178,188],"0.01":[179],"(95%":[180,190],"CI":[181,191],"[0.81,":[182],"0.84])":[183],"AUC":[185],"0.88":[187],"0.02":[189],"[0.85,":[192],"0.89])":[193],"over":[196],"dataset.":[199],"best":[201],"result":[202],"reached":[204],"fold":[206],"3":[207],"(0.84":[208],"0.89":[210],"AUC,":[214],"respectively).":[215],"Positive":[216],"results":[217],"evaluated":[219],"by":[220],"detector,":[223],"which":[224],"mAP":[227],"59.51%.":[229],"Thus,":[230],"good":[232],"performance":[233],"rapid":[235],"prediction":[237],"make":[238],"system":[240],"promising":[242],"means":[243],"assist":[245],"radiologists":[246],"decision":[248],"making":[249],"tasks.":[250]},"counts_by_year":[],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}
