{"id":"https://openalex.org/W3160667983","doi":"https://doi.org/10.1109/ssd52085.2021.9429383","title":"Deep Learning Approach for COVID-19 Detection Based on X-Ray Images","display_name":"Deep Learning Approach for COVID-19 Detection Based on X-Ray Images","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3160667983","doi":"https://doi.org/10.1109/ssd52085.2021.9429383","mag":"3160667983"},"language":"en","primary_location":{"id":"doi:10.1109/ssd52085.2021.9429383","is_oa":true,"landing_page_url":"https://doi.org/10.1109/ssd52085.2021.9429383","pdf_url":"https://ieeexplore.ieee.org/ielx7/9429268/9429289/09429383.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Multi-Conference on Systems, Signals &amp; Devices (SSD)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/9429268/9429289/09429383.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074323952","display_name":"Hayat O. Alasasfeh","orcid":null},"institutions":[{"id":"https://openalex.org/I156983542","display_name":"Jordan University of Science and Technology","ror":"https://ror.org/03y8mtb59","country_code":"JO","type":"education","lineage":["https://openalex.org/I156983542"]}],"countries":["JO"],"is_corresponding":true,"raw_author_name":"Hayat O. Alasasfeh","raw_affiliation_strings":["Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan"],"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan","institution_ids":["https://openalex.org/I156983542"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033378771","display_name":"Taqwa Alomari","orcid":null},"institutions":[{"id":"https://openalex.org/I156983542","display_name":"Jordan University of Science and Technology","ror":"https://ror.org/03y8mtb59","country_code":"JO","type":"education","lineage":["https://openalex.org/I156983542"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"Taqwa Alomari","raw_affiliation_strings":["Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan"],"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan","institution_ids":["https://openalex.org/I156983542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5097106790","display_name":"MS Ibbini","orcid":null},"institutions":[{"id":"https://openalex.org/I156983542","display_name":"Jordan University of Science and Technology","ror":"https://ror.org/03y8mtb59","country_code":"JO","type":"education","lineage":["https://openalex.org/I156983542"]}],"countries":["JO"],"is_corresponding":false,"raw_author_name":"MS Ibbini","raw_affiliation_strings":["Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan"],"affiliations":[{"raw_affiliation_string":"Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan","institution_ids":["https://openalex.org/I156983542"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074323952"],"corresponding_institution_ids":["https://openalex.org/I156983542"],"apc_list":null,"apc_paid":null,"fwci":1.0996,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76735176,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9785000085830688,"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":0.9768000245094299,"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/deep-learning","display_name":"Deep learning","score":0.822851300239563},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7767583727836609},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7110427021980286},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.6630114912986755},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6391344666481018},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4639676809310913},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46253564953804016},{"id":"https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score":0.43245095014572144},{"id":"https://openalex.org/keywords/pandemic","display_name":"Pandemic","score":0.4301875829696655},{"id":"https://openalex.org/keywords/pneumonia","display_name":"Pneumonia","score":0.42667803168296814},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.384152889251709},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35119372606277466},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.2582651972770691},{"id":"https://openalex.org/keywords/infectious-disease","display_name":"Infectious disease (medical specialty)","score":0.2469140589237213},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.24520820379257202},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.21099674701690674}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.822851300239563},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7767583727836609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7110427021980286},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.6630114912986755},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6391344666481018},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4639676809310913},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46253564953804016},{"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":0.43245095014572144},{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.4301875829696655},{"id":"https://openalex.org/C2777914695","wikidata":"https://www.wikidata.org/wiki/Q12192","display_name":"Pneumonia","level":2,"score":0.42667803168296814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.384152889251709},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35119372606277466},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.2582651972770691},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.2469140589237213},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.24520820379257202},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.21099674701690674},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssd52085.2021.9429383","is_oa":true,"landing_page_url":"https://doi.org/10.1109/ssd52085.2021.9429383","pdf_url":"https://ieeexplore.ieee.org/ielx7/9429268/9429289/09429383.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Multi-Conference on Systems, Signals &amp; Devices (SSD)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/ssd52085.2021.9429383","is_oa":true,"landing_page_url":"https://doi.org/10.1109/ssd52085.2021.9429383","pdf_url":"https://ieeexplore.ieee.org/ielx7/9429268/9429289/09429383.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Multi-Conference on Systems, Signals &amp; Devices (SSD)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8299999833106995,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3160667983.pdf","grobid_xml":"https://content.openalex.org/works/W3160667983.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2194775991","https://openalex.org/W2491014331","https://openalex.org/W2531409750","https://openalex.org/W2612445135","https://openalex.org/W2782501758","https://openalex.org/W2949650786","https://openalex.org/W2962835968","https://openalex.org/W2972901606","https://openalex.org/W3003790823","https://openalex.org/W3007497549","https://openalex.org/W3009333602","https://openalex.org/W3009906937","https://openalex.org/W3010197775","https://openalex.org/W3010233963","https://openalex.org/W3011102168","https://openalex.org/W3012751338","https://openalex.org/W3013019084","https://openalex.org/W3013308762","https://openalex.org/W3013564598","https://openalex.org/W4285719527","https://openalex.org/W4297775537","https://openalex.org/W6637373629","https://openalex.org/W6775884374"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"COVID-19":[0],"is":[1,16,50,57,103,118,126,180],"an":[2,165,181],"infectious":[3],"disease":[4,49],"that":[5,27,102,125,162,177],"has":[6,142],"invaded":[7],"the":[8,23,29,74,77,150,198,203],"world":[9],"since":[10],"2019":[11],"starting":[12],"from":[13,139,205],"China.":[14],"It":[15],"caused":[17],"by":[18,53],"a":[19,92,119],"new":[20],"member":[21],"of":[22,32,79,167,187],"corona":[24],"viruses'":[25],"family":[26],"attacks":[28],"respiratory":[30],"system":[31,124],"living":[33],"body":[34],"leading":[35],"to":[36,42,90,105,196],"fever,":[37],"cough,":[38],"general":[39],"tiredness":[40],"and":[41,60,68,81,114,121,131,145,157,172,201],"death":[43],"in":[44,84,149],"worst":[45],"case":[46],"scenarios.":[47],"The":[48,63],"commonly":[51],"detected":[52],"RT-PCR":[54],"tests,":[55],"which":[56],"time":[58],"consuming":[59],"relatively":[61],"expensive.":[62],"need":[64],"for":[65,76,184],"faster,":[66],"cheaper,":[67],"more":[69],"precise":[70],"diagnostic":[71],"tool":[72],"raised":[73],"demand":[75],"utilization":[78],"technology":[80],"artificial":[82],"intelligence":[83],"this":[85],"purpose.":[86,152],"This":[87],"study":[88],"aims":[89],"build":[91],"robust":[93],"deep":[94,178],"learning":[95,179],"algorithm":[96],"using":[97],"convolutional":[98],"neural":[99],"networks":[100],"(CNNs)":[101],"capable":[104],"classify":[106],"chest":[107],"X-ray":[108,117],"images":[109],"into":[110],"COVID-19,":[111,188],"viral":[112],"pneumonia,":[113],"normal":[115],"cases.":[116],"safe":[120],"inexpensive":[122],"imaging":[123],"available":[127],"at":[128],"all":[129],"hospitals":[130],"healthcare":[132,199],"centers.":[133],"A":[134],"novel":[135],"CNN":[136],"model":[137],"built":[138],"scratch":[140],"(COV-X)":[141],"been":[143],"introduced":[144],"shown":[146,164],"94%":[147],"accuracy":[148,166],"classification":[151],"VGG16,":[153],"VGG19,":[154],"RESNET50,":[155],"XCEPTION,":[156],"MOBILENET":[158],"are":[159],"pre-trained":[160],"models":[161],"have":[163],"95%,":[168,169],"33%,":[170],"65%,":[171],"77%,":[173],"respectively.":[174],"Findings":[175],"prove":[176],"effective":[182],"technique":[183],"early":[185],"detection":[186,192],"it":[189],"provides":[190],"automatic":[191],"with":[193],"high":[194],"reliability":[195],"help":[197],"professions":[200],"avoid":[202],"pandemic":[204],"spreading":[206],"more.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
