{"id":"https://openalex.org/W4289109135","doi":"https://doi.org/10.1007/s11042-022-13486-8","title":"Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images","display_name":"Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images","publication_year":2022,"publication_date":"2022-08-01","ids":{"openalex":"https://openalex.org/W4289109135","doi":"https://doi.org/10.1007/s11042-022-13486-8","pmid":"https://pubmed.ncbi.nlm.nih.gov/35938148"},"language":"en","primary_location":{"id":"doi:10.1007/s11042-022-13486-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-022-13486-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-13486-8.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-13486-8.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100638278","display_name":"Ajay Sharma","orcid":"https://orcid.org/0000-0003-3288-531X"},"institutions":[{"id":"https://openalex.org/I91357014","display_name":"Banaras Hindu University","ror":"https://ror.org/04cdn2797","country_code":"IN","type":"education","lineage":["https://openalex.org/I91357014"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ajay Sharma","raw_affiliation_strings":["Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005 India","Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005 India","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005, India","institution_ids":["https://openalex.org/I91357014"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081133318","display_name":"Pramod Kumar Mishra","orcid":"https://orcid.org/0000-0003-3957-1161"},"institutions":[{"id":"https://openalex.org/I91357014","display_name":"Banaras Hindu University","ror":"https://ror.org/04cdn2797","country_code":"IN","type":"education","lineage":["https://openalex.org/I91357014"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pramod Kumar Mishra","raw_affiliation_strings":["Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005 India","Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005 India","institution_ids":[]},{"raw_affiliation_string":"Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, 221005, India","institution_ids":["https://openalex.org/I91357014"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100638278"],"corresponding_institution_ids":["https://openalex.org/I91357014"],"apc_list":null,"apc_paid":null,"fwci":5.4749,"has_fulltext":true,"cited_by_count":40,"citation_normalized_percentile":{"value":0.9657796,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"81","issue":"29","first_page":"42649","last_page":"42690"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9902999997138977,"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/T10862","display_name":"AI in cancer detection","score":0.9886000156402588,"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/computer-science","display_name":"Computer science","score":0.7526865601539612},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.7046893239021301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.634312629699707},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5179161429405212},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49527063965797424},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.48048439621925354},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4787020981311798},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.46880513429641724},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.45287570357322693},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4414716958999634},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4366365969181061},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35328418016433716},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1799246370792389},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12136319279670715}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7526865601539612},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.7046893239021301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.634312629699707},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5179161429405212},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49527063965797424},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.48048439621925354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4787020981311798},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.46880513429641724},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.45287570357322693},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4414716958999634},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4366365969181061},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35328418016433716},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1799246370792389},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12136319279670715},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"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/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"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/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s11042-022-13486-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-022-13486-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-13486-8.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},{"id":"pmid:35938148","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35938148","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia tools and applications","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:9340712","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9340712","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Multimed Tools Appl","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1007/s11042-022-13486-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-022-13486-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11042-022-13486-8.pdf","source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321447","display_name":"Banaras Hindu University","ror":"https://ror.org/04cdn2797"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4289109135.pdf","grobid_xml":"https://content.openalex.org/works/W4289109135.grobid-xml"},"referenced_works_count":73,"referenced_works":["https://openalex.org/W1904878066","https://openalex.org/W2006210519","https://openalex.org/W2007222021","https://openalex.org/W2054496749","https://openalex.org/W2116973876","https://openalex.org/W2142514727","https://openalex.org/W2156110830","https://openalex.org/W2271974312","https://openalex.org/W2611650229","https://openalex.org/W2811365054","https://openalex.org/W2886584550","https://openalex.org/W2901954625","https://openalex.org/W2928842276","https://openalex.org/W2952587392","https://openalex.org/W2963466845","https://openalex.org/W3000516737","https://openalex.org/W3004227146","https://openalex.org/W3005987013","https://openalex.org/W3013601031","https://openalex.org/W3016488464","https://openalex.org/W3017521469","https://openalex.org/W3017855299","https://openalex.org/W3018775276","https://openalex.org/W3023402713","https://openalex.org/W3024801014","https://openalex.org/W3025582345","https://openalex.org/W3028121689","https://openalex.org/W3033616466","https://openalex.org/W3036638392","https://openalex.org/W3039545596","https://openalex.org/W3039563973","https://openalex.org/W3041148517","https://openalex.org/W3045460727","https://openalex.org/W3046144462","https://openalex.org/W3046939034","https://openalex.org/W3047926930","https://openalex.org/W3048670851","https://openalex.org/W3048749423","https://openalex.org/W3083753334","https://openalex.org/W3086039674","https://openalex.org/W3087300877","https://openalex.org/W3088154786","https://openalex.org/W3091252743","https://openalex.org/W3095676075","https://openalex.org/W3098809754","https://openalex.org/W3101156210","https://openalex.org/W3102435842","https://openalex.org/W3105081694","https://openalex.org/W3105777051","https://openalex.org/W3107917147","https://openalex.org/W3112390123","https://openalex.org/W3118116903","https://openalex.org/W3118977279","https://openalex.org/W3120139461","https://openalex.org/W3120327591","https://openalex.org/W3122453825","https://openalex.org/W3124375766","https://openalex.org/W3125811498","https://openalex.org/W3126395150","https://openalex.org/W3128041674","https://openalex.org/W3128189270","https://openalex.org/W3129440340","https://openalex.org/W3133930341","https://openalex.org/W3154084386","https://openalex.org/W3171929138","https://openalex.org/W3173535844","https://openalex.org/W3184822525","https://openalex.org/W3194296141","https://openalex.org/W4213008694","https://openalex.org/W4213315373","https://openalex.org/W4281758010","https://openalex.org/W6600120041","https://openalex.org/W6603287499"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W4230315250","https://openalex.org/W2086519370","https://openalex.org/W2087343574"],"abstract_inverted_index":{"The":[0,186],"outbreak":[1],"of":[2,35,44,56,69,78,121,212,222,226,239,243,247,251],"novel":[3],"coronavirus":[4],"(COVID-19)":[5],"disease":[6],"has":[7,267],"infected":[8],"more":[9,129],"than":[10],"135.6":[11],"million":[12],"people":[13],"globally.":[14],"For":[15,128],"its":[16],"early":[17],"diagnosis,":[18],"researchers":[19],"consider":[20],"chest":[21],"X-ray":[22,87],"examinations":[23],"as":[24],"a":[25],"standard":[26],"screening":[27],"technique":[28],"in":[29,53,119,125,162,217,286],"addition":[30],"to":[31,65,271,289],"RT-PCR":[32],"test.":[33],"Majority":[34],"research":[36],"work":[37],"till":[38],"date":[39],"focused":[40],"only":[41,276],"on":[42,75,182],"application":[43],"deep":[45,79,257],"learning":[46,80,258],"approaches":[47,74],"that":[48,256],"is":[49],"relevant":[50],"but":[51,284],"lacking":[52],"better":[54],"pre-processing":[55],"CXR":[57],"images.":[58],"Towards":[59],"this":[60,62],"direction,":[61],"study":[63,115,254],"aims":[64],"explore":[66],"cumulative":[67],"effects":[68],"image":[70],"denoising":[71,112],"and":[72,92,99,110,141,173,199,224,249,263,281],"enhancement":[73,143],"the":[76],"performance":[77,138,269],"approaches.":[81,144],"Regarding":[82],"pre-processing,":[83],"suitable":[84],"methods":[85],"for":[86,137],"images,":[88],"Histogram":[89],"equalization,":[90],"CLAHE":[91],"gamma":[93,261],"correction":[94,262],"have":[95],"been":[96],"tested":[97],"individually":[98],"along":[100],"with":[101,140,190,205,214,233,241,260],"adaptive":[102],"median":[103,105],"filter,":[104,106],"total":[107],"variation":[108],"filter":[109],"gaussian":[111],"filters.":[113],"Proposed":[114,253],"compared":[116,133,270],"eleven":[117],"combinations":[118],"exploration":[120],"most":[122],"coherent":[123],"approach":[124],"greedy":[126],"manner.":[127],"robust":[130],"analysis,":[131],"we":[132],"ten":[134],"CNN":[135],"architectures":[136],"evaluation":[139],"without":[142],"These":[145,158],"models":[146,159,194],"are":[147,160],"InceptionV3,":[148],"InceptionResNetV2,":[149],"MobileNet,":[150],"MobileNetV2,":[151],"Vgg19,":[152],"NASNetMobile,":[153],"ResNet101,":[154],"DenseNet121,":[155],"DenseNet169,":[156],"DenseNet201.":[157],"trained":[161],"4-way":[163,202],"(COVID-19":[164,177],"pneumonia":[165,170,283],"vs":[166,168,171,178,180],"Viral":[167],"Bacterial":[169],"Normal)":[172,181],"3-way":[174,229],"classification":[175,197,203,230],"scenario":[176],"Pneumonia":[179],"two":[183],"benchmark":[184],"datasets.":[185],"proposed":[187],"methodology":[188],"determines":[189],"TVF":[191,206,234,264],"+":[192,207,235,265],"Gamma,":[193],"achieve":[195],"higher":[196],"accuracy":[198,211,218,238],"sensitivity.":[200],"In":[201,228],"MobileNet":[204],"Gamma":[208,236,266],"achieves":[209],"top":[210],"93.25%":[213],"1.91%":[215],"improvement":[216,242],"score,":[219],"COVID-19":[220,245,280],"sensitivity":[221,246],"98.72%":[223],"F1-score":[225,250],"92.14%.":[227],"our":[231],"DenseNet201":[232],"gains":[237],"91.10%":[240],"1.47%,":[244],"100%":[248],"91.09%.":[252],"concludes":[255],"modes":[259],"superior":[268],"state-of-the-art":[272],"models.":[273],"This":[274],"not":[275],"minimizes":[277],"overlapping":[278],"between":[279],"virus":[282],"advantageous":[285],"time":[287],"required":[288],"converge":[290],"best":[291],"possible":[292],"results.":[293]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}