{"id":"https://openalex.org/W4220889907","doi":"https://doi.org/10.1117/12.2613002","title":"Comparison of deep learning architectures for COVID-19 diagnosis using chest X-ray images","display_name":"Comparison of deep learning architectures for COVID-19 diagnosis using chest X-ray images","publication_year":2022,"publication_date":"2022-03-31","ids":{"openalex":"https://openalex.org/W4220889907","doi":"https://doi.org/10.1117/12.2613002"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2613002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5052803929","display_name":"Denilson Alejandro Samp\u00e9n Dedi\u00f3s","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Denilson Alejandro Samp\u00e9n Dedi\u00f3s","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038540622","display_name":"Roberto Lavarello","orcid":"https://orcid.org/0000-0001-8472-5161"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roberto Janniel Lavarello Montero","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":60},"biblio":{"volume":null,"issue":null,"first_page":"40","last_page":"40"},"is_retracted":false,"is_paratext":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":0.9819,"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/T10041","display_name":"COVID-19 Clinical Research Studies","score":0.9759,"subfield":{"id":"https://openalex.org/subfields/2725","display_name":"Infectious Diseases"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.8004533},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71359396},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6942875},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.64922965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6228454},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.49197719},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44320032},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.42027333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40299702},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.13918099},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0846951},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"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":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2613002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.82,"id":"https://metadata.un.org/sdg/3"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":13,"referenced_works":["https://openalex.org/W2788633781","https://openalex.org/W3005827208","https://openalex.org/W3017855299","https://openalex.org/W3036638392","https://openalex.org/W3043087541","https://openalex.org/W3043913272","https://openalex.org/W3045852551","https://openalex.org/W3048917590","https://openalex.org/W3048920434","https://openalex.org/W3105081694","https://openalex.org/W3193332839","https://openalex.org/W4287756648","https://openalex.org/W4394654459"],"related_works":["https://openalex.org/W4225593417","https://openalex.org/W410723623","https://openalex.org/W3160494304","https://openalex.org/W3022298670","https://openalex.org/W3006162251","https://openalex.org/W2573498121","https://openalex.org/W2413243053","https://openalex.org/W2118717649","https://openalex.org/W2035068594","https://openalex.org/W2015341305"],"abstract_inverted_index":{"The":[0,47],"implementation":[1],"of":[2,26,40,44,53,91,105,145,178,187],"architectures":[3,21,57,71,148,157],"based":[4],"on":[5],"artificial":[6],"intelligence":[7],"and":[8,60,110,149,162,166,180],"deep":[9,55],"learning":[10,56],"to":[11,81,133],"support":[12,170],"COVID-19":[13,45,63],"diagnosis":[14,64],"has":[15],"great":[16],"potential.":[17],"However,":[18],"especially":[19],"in":[20,88],"designed":[22],"at":[23],"the":[24,27,70,75,79,83,89,92,126,129,135,143,146,156,159,184,188],"beginning":[25],"pandemic,":[28],"they":[29],"use":[30],"different":[31,123],"databases":[32,76,120],"that":[33,155],"do":[34],"not":[35],"contain":[36],"a":[37,51,95,169],"good":[38],"amount":[39],"chest":[41,66,102],"X-ray":[42,103],"images":[43,104,113],"patients.":[46],"present":[48],"work":[49],"presents":[50],"comparison":[52],"three":[54],"(COVID-Net,":[58],"CovXNet":[59,167],"DarkCovidNet)":[61],"for":[62,114,183],"using":[65,119],"Xray":[67],"images.":[68],"First,":[69],"were":[72],"implemented":[73],"with":[74,85,98,107,158,168,175],"provided":[77],"by":[78],"authors,":[80],"compare":[82,150],"results":[84],"those":[86],"presented":[87],"state":[90],"art.":[93],"Then,":[94],"new":[96,189],"database":[97,130],"more":[99],"than":[100],"9000":[101],"patients":[106],"COVID-19,":[108],"pneumonia":[109],"healthy":[111],"(3305":[112],"each":[115],"class),":[116],"was":[117,131,153],"elaborated":[118],"from":[121],"four":[122],"institutions":[124],"around":[125],"world.":[127],"Finally,":[128],"used":[132],"evaluate":[134,142],"original":[136],"architectures,":[137],"retrain":[138],"them":[139],"and,":[140],"finally,":[141],"performance":[144,161],"retrained":[147],"results.":[151],"It":[152],"identified":[154],"best":[160],"generalizability":[163],"are":[164],"DarkCovidNet":[165],"vector":[171],"machine":[172],"stacking":[173],"algorithm,":[174],"an":[176],"accuracy":[177],"94.04%":[179],"92.02%":[181],"respectively,":[182],"test":[185],"data":[186],"database.":[190]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4220889907","counts_by_year":[],"updated_date":"2024-12-10T02:35:31.839805","created_date":"2022-04-03"}