{"id":"https://openalex.org/W4391936095","doi":"https://doi.org/10.1109/tce.2024.3367489","title":"A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images","display_name":"A New Classification Method for Diagnosing COVID-19 Pneumonia via Joint Parallel Deformable MLP Modules and Bi-LSTM With Multi-Source Generated Data of CXR Images","publication_year":2024,"publication_date":"2024-02-01","ids":{"openalex":"https://openalex.org/W4391936095","doi":"https://doi.org/10.1109/tce.2024.3367489"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2024.3367489","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tce.2024.3367489","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-article"},"type":"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/A5040899512","display_name":"Yiwen Liu","orcid":"https://orcid.org/0009-0002-2851-4604"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210132426","display_name":"Shanghai Fudan Microelectronics (China)","ror":"https://ror.org/02vfj3j86","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210132426"]},{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yiwen Liu","raw_affiliation_strings":["School of Information Science and Technology, Donghua University, Shanghai, China","School of Microelectronics, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Donghua University, Shanghai, China","institution_ids":["https://openalex.org/I181326427"]},{"raw_affiliation_string":"School of Microelectronics, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I4210132426","https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023662019","display_name":"Wenyu Xing","orcid":"https://orcid.org/0000-0001-7170-9488"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenyu Xing","raw_affiliation_strings":["Academy for Engineering and Technology, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Academy for Engineering and Technology, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000776140","display_name":"Mingquan Lin","orcid":"https://orcid.org/0000-0003-0862-6588"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingquan Lin","raw_affiliation_strings":["Department of Surgery, University of Minnesota, Minneapolis, MN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Surgery, University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673298","display_name":"Yuping Liu","orcid":"https://orcid.org/0009-0006-3203-7360"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]},{"id":"https://openalex.org/I4210132426","display_name":"Shanghai Fudan Microelectronics (China)","ror":"https://ror.org/02vfj3j86","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210132426"]},{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuping Liu","raw_affiliation_strings":["School of Information Science and Technology, Donghua University, Shanghai, China","School of Microelectronics, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Donghua University, Shanghai, China","institution_ids":["https://openalex.org/I181326427"]},{"raw_affiliation_string":"School of Microelectronics, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I4210132426","https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050716572","display_name":"Tommy W. S. Chow","orcid":"https://orcid.org/0000-0001-7051-0434"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Tommy W. S. Chow","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, Hong Kong, S.A.R, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, Hong Kong, S.A.R, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040899512"],"corresponding_institution_ids":["https://openalex.org/I181326427","https://openalex.org/I24943067","https://openalex.org/I4210132426"],"apc_list":null,"apc_paid":null,"fwci":0.4307,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59174881,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"70","issue":"1","first_page":"2794","last_page":"2805"},"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":0.9639999866485596,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9513999819755554,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7011911869049072},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6710065007209778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6195164322853088},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5895045399665833},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5615355968475342},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.5567206144332886},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5543578267097473},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48707106709480286},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4789572060108185},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44416943192481995},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4361534118652344},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09426504373550415},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.09344014525413513}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7011911869049072},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6710065007209778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6195164322853088},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5895045399665833},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5615355968475342},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.5567206144332886},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5543578267097473},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48707106709480286},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4789572060108185},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44416943192481995},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4361534118652344},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09426504373550415},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.09344014525413513},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2024.3367489","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tce.2024.3367489","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1327405719","display_name":null,"funder_award_id":"62073272","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W2982083293","https://openalex.org/W3012211282","https://openalex.org/W3014725478","https://openalex.org/W3016610966","https://openalex.org/W3017855299","https://openalex.org/W3018757597","https://openalex.org/W3023402713","https://openalex.org/W3033616466","https://openalex.org/W3036552116","https://openalex.org/W3036638392","https://openalex.org/W3037914312","https://openalex.org/W3044223324","https://openalex.org/W3044240928","https://openalex.org/W3045460727","https://openalex.org/W3085943693","https://openalex.org/W3088680016","https://openalex.org/W3094502228","https://openalex.org/W3097211536","https://openalex.org/W3101978680","https://openalex.org/W3104810384","https://openalex.org/W3112175976","https://openalex.org/W3121523901","https://openalex.org/W3128189270","https://openalex.org/W3155270026","https://openalex.org/W3174532863","https://openalex.org/W3183916646","https://openalex.org/W3211191383","https://openalex.org/W3216543548","https://openalex.org/W4205942531","https://openalex.org/W4210711107","https://openalex.org/W4224287119","https://openalex.org/W4285186299","https://openalex.org/W4285661751","https://openalex.org/W4288048173","https://openalex.org/W4293328490","https://openalex.org/W4295046720","https://openalex.org/W4308160156","https://openalex.org/W4312820606","https://openalex.org/W4312881242","https://openalex.org/W4313531537","https://openalex.org/W4313889858","https://openalex.org/W4320009770","https://openalex.org/W4321609173","https://openalex.org/W4328107847","https://openalex.org/W4362597800","https://openalex.org/W4379619220","https://openalex.org/W4385223980","https://openalex.org/W4386083042","https://openalex.org/W4386181406","https://openalex.org/W6795140394","https://openalex.org/W6811014117"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2082756648","https://openalex.org/W3194278305","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"The":[0,206,265],"Coronavirus":[1],"Disease":[2],"2019":[3],"(COVID-19)":[4],"pneumonia":[5,118],"poses":[6],"a":[7,62,98,170],"critical":[8],"threat":[9],"to":[10,25,29,73,80,112,194,244,257],"public":[11],"health":[12],"because":[13],"of":[14,32,40,47,89,123,154,161,221,261,292,302],"its":[15],"powerful":[16],"infectiousness,":[17],"which":[18,175,287],"has":[19],"spurred":[20],"the":[21,30,56,75,81,93,121,152,158,162,196,202,219,232,259,262,269,289,293,300],"consumer":[22],"electronics":[23],"industry":[24],"innovate":[26],"rapidly,":[27],"leading":[28],"emergence":[31],"advanced":[33],"diagnostic":[34],"and":[35,116,129,142,167,179,190,212,218,239,280],"monitoring":[36],"devices.":[37],"Automated":[38],"detection":[39],"COVID-19":[41,67,246,305],"based":[42,250],"on":[43,151,223,251],"multi-source":[44,106],"generated":[45,107,155,198],"data":[46,199,207],"CXR":[48,163,253],"images":[49,178,254],"is":[50,71,111,165,208,242],"therefore":[51],"essential":[52],"for":[53,77,200,296],"prevention.":[54],"In":[55],"clinic,":[57],"chest":[58,159],"X-ray":[59],"(CXR)":[60],"plays":[61],"pivotal":[63],"role":[64],"in":[65,299],"diagnosing":[66],"pneumonia.":[68,90,247,306],"However,":[69],"it":[70],"challenging":[72],"utilize":[74],"dataset":[76],"diagnosis":[78,301],"attributed":[79],"significant":[82,290],"imaging":[83],"similarities":[84],"observed":[85],"among":[86],"various":[87],"types":[88],"To":[91],"address":[92],"aforementioned":[94],"issue,":[95],"we":[96],"present":[97],"pioneering":[99],"classification":[100,204],"model,":[101],"denoted":[102],"as":[103],"PDMLP-Bi-LSTM,":[104],"leveraging":[105],"data.":[108],"Its":[109],"objective":[110],"discriminate":[113],"Normal,":[114],"COVID-19,":[115],"Other":[117],"cases.":[119],"Through":[120],"fusion":[122],"parallel":[124,147,224],"deformable":[125,215],"multi-layer":[126],"perceptrons":[127],"(MLPs)":[128],"Bi-directional":[130],"Long":[131],"Short-Term":[132],"Memory":[133],"(Bi-LSTM)":[134],"modules,":[135,217],"this":[136],"model":[137],"extracts":[138],"multi-level":[139],"abstract":[140],"features":[141,222],"investigates":[143],"potential":[144,291],"correlations":[145],"between":[146],"output":[148],"features,":[149],"capitalizing":[150],"wealth":[153],"information.":[156],"Initially,":[157],"region":[160],"image":[164,188],"localized":[166],"cropped":[168],"using":[169,186,228],"pre-trained":[171],"YOLO-V4":[172],"network,":[173],"through":[174],"13-dimensional":[176],"transformed":[177],"16-dimensional":[180],"depth":[181],"feature":[182],"maps":[183],"are":[184,226],"extracted":[185],"traditional":[187],"filters":[189],"convolutional":[191],"neural":[192],"network":[193],"form":[195],"30-dimensional":[197],"training":[201],"proposed":[203,263,270,294],"model.":[205],"then":[209],"fed":[210],"spatially":[211],"channel-wise":[213],"into":[214],"MLP":[216],"relationships":[220],"channels":[225],"analyzed":[227],"Bi-LSTM":[229],"modules.":[230],"Finally,":[231],"classifier":[233],"formed":[234],"by":[235,282],"fully":[236],"connected":[237],"layers":[238],"SoftMax":[240],"function":[241],"employed":[243],"diagnose":[245],"Extensive":[248],"simulations":[249],"4099":[252],"were":[255],"conducted":[256],"validate":[258],"performance":[260,274],"method.":[264],"results":[266],"indicated":[267],"that":[268],"method":[271,295],"exhibits":[272],"excellent":[273],"with":[275,304],"accuracy,":[276],"specificity,":[277],"precision,":[278],"recall,":[279],"F1-score":[281],"approximately":[283],"98%":[284],"or":[285],"above,":[286],"demonstrates":[288],"clinically":[297],"aiding":[298],"patients":[303]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}