{"id":"https://openalex.org/W3216038854","doi":"https://doi.org/10.1109/healthcom49281.2021.9615913","title":"Deep U-Net Network for identifying Covid 19 infection Using X Ray Images","display_name":"Deep U-Net Network for identifying Covid 19 infection Using X Ray Images","publication_year":2021,"publication_date":"2021-03-01","ids":{"openalex":"https://openalex.org/W3216038854","doi":"https://doi.org/10.1109/healthcom49281.2021.9615913","mag":"3216038854"},"language":"en","primary_location":{"id":"doi:10.1109/healthcom49281.2021.9615913","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom49281.2021.9615913","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on E-health Networking, Application &amp; Services (HEALTHCOM)","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/A5109526895","display_name":"Somasundharam Devaraj","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Somasundharam Devaraj","raw_affiliation_strings":["Department of Biomedical Engineering, Sri Shakthi institute of Engineering and Technology, Coimbatore, Tamilnadu, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Sri Shakthi institute of Engineering and Technology, Coimbatore, Tamilnadu, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056066129","display_name":"M. Nirmala Madian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M. Nirmala Madian","raw_affiliation_strings":["Department of ECE, Sri Shakthi institute of Engineering and Technology, Coimbatore, Tamilnadu, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of ECE, Sri Shakthi institute of Engineering and Technology, Coimbatore, Tamilnadu, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1922,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.79339502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"25","issue":null,"first_page":"1","last_page":"5"},"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/T10862","display_name":"AI in cancer detection","score":0.9957000017166138,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9929999709129333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.6916104555130005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6688665151596069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5672126412391663},{"id":"https://openalex.org/keywords/pneumonia","display_name":"Pneumonia","score":0.547304093837738},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5249543190002441},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5089013576507568},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4948844313621521},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4468638300895691},{"id":"https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score":0.43804556131362915},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3003508448600769},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.23534172773361206},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.10546538233757019},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.06704142689704895}],"concepts":[{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.6916104555130005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6688665151596069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5672126412391663},{"id":"https://openalex.org/C2777914695","wikidata":"https://www.wikidata.org/wiki/Q12192","display_name":"Pneumonia","level":2,"score":0.547304093837738},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5249543190002441},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5089013576507568},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4948844313621521},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4468638300895691},{"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":0.43804556131362915},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3003508448600769},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.23534172773361206},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.10546538233757019},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.06704142689704895},{"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:10.1109/healthcom49281.2021.9615913","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom49281.2021.9615913","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on E-health Networking, Application &amp; Services (HEALTHCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1500359668","https://openalex.org/W1811661006","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2095705004","https://openalex.org/W2517954747","https://openalex.org/W3001118548","https://openalex.org/W3006007867","https://openalex.org/W3006028741","https://openalex.org/W3006111207","https://openalex.org/W3007446911","https://openalex.org/W3007497549","https://openalex.org/W3009946942","https://openalex.org/W3011041531","https://openalex.org/W3011810856","https://openalex.org/W3012054129","https://openalex.org/W3037538421","https://openalex.org/W6639824700","https://openalex.org/W6674330103","https://openalex.org/W6774270847","https://openalex.org/W6774787700"],"related_works":["https://openalex.org/W4206669628","https://openalex.org/W4224279380","https://openalex.org/W4205317059","https://openalex.org/W3176864053","https://openalex.org/W4206651655","https://openalex.org/W4206548596","https://openalex.org/W4292098121","https://openalex.org/W4210433452","https://openalex.org/W3036314732","https://openalex.org/W3084808338"],"abstract_inverted_index":{"The":[0,37,53,121],"Novel":[1],"coronavirus":[2],"(COVID-19)":[3],"detection":[4],"is":[5,40,55,79],"a":[6,76],"challenging":[7],"task":[8],"for":[9,57,81,119],"physicians":[10],"and":[11,43,69,83,103],"clinical":[12,22],"people.":[13],"Due":[14],"the":[15,21,31,35,41,48,61,117,125],"higher":[16],"rate":[17],"of":[18,27,34,85,127],"affected":[19],"people,":[20],"tests":[23],"take":[24],"too":[25],"much":[26],"time":[28],"to":[29,46,66,90,115],"provide":[30],"test":[32,111],"results":[33],"patients.":[36],"alternate":[38],"screening":[39],"X-ray":[42,88,99],"CT":[44,63],"images":[45,64,89,106,112],"detect":[47],"infection":[49],"in":[50,60],"respiratory":[51,71],"system.":[52],"classification":[54,84],"complex":[56],"visual":[58],"examination":[59],"X-ray,":[62],"due":[65],"COVID,":[67],"Pneumonia":[68],"other":[70,91],"problems.":[72],"In":[73,94],"this":[74,95],"paper,":[75],"novel":[77],"Unet":[78],"proposed":[80,122],"segmentation":[82],"COVID":[86,98],"with":[87],"diseased":[92],"images.":[93],"analysis":[96],"2000":[97,101,104],"images,":[100],"bacteria":[102],"normal":[105],"are":[107,113],"used.":[108],"Overall,":[109],"6000":[110],"considered":[114],"train":[116],"network":[118],"classification.":[120],"method":[123],"provides":[124],"accuracy":[126],"98.49":[128],"%.":[129]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
