{"id":"https://openalex.org/W3096950781","doi":"https://doi.org/10.1007/s10489-020-02019-1","title":"A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks","display_name":"A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks","publication_year":2020,"publication_date":"2020-11-04","ids":{"openalex":"https://openalex.org/W3096950781","doi":"https://doi.org/10.1007/s10489-020-02019-1","mag":"3096950781","pmid":"https://pubmed.ncbi.nlm.nih.gov/34764560"},"language":"en","primary_location":{"id":"doi:10.1007/s10489-020-02019-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-020-02019-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-020-02019-1.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","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/s10489-020-02019-1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041685747","display_name":"H\u00fcseyin Ya\u015far","orcid":"https://orcid.org/0000-0002-7583-980X"},"institutions":[{"id":"https://openalex.org/I1303077703","display_name":"Ministry of Health","ror":"https://ror.org/00pkvys92","country_code":"TR","type":"government","lineage":["https://openalex.org/I1303077703"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Huseyin Yasar","raw_affiliation_strings":["Ministry of Health of Republic of Turkey, Ankara, Turkey"],"affiliations":[{"raw_affiliation_string":"Ministry of Health of Republic of Turkey, Ankara, Turkey","institution_ids":["https://openalex.org/I1303077703"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022115380","display_name":"Murat Ceylan","orcid":"https://orcid.org/0000-0001-6503-9668"},"institutions":[{"id":"https://openalex.org/I4210117254","display_name":"Konya Technical University","ror":"https://ror.org/02s82rs08","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210117254"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Murat Ceylan","raw_affiliation_strings":["Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, Konya Technical University, Konya, Turkey"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, Konya Technical University, Konya, Turkey","institution_ids":["https://openalex.org/I4210117254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5041685747"],"corresponding_institution_ids":["https://openalex.org/I1303077703"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":3.012,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.93315303,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"51","issue":"5","first_page":"2740","last_page":"2763"},"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9796000123023987,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9722999930381775,"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":0.8088656663894653},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7729876637458801},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7351871728897095},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6725291609764099},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6518844366073608},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5705091953277588},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5166628360748291},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4789167642593384},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4383787214756012},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4292992353439331},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.3578492999076843},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.0737677812576294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8088656663894653},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7729876637458801},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7351871728897095},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6725291609764099},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6518844366073608},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5705091953277588},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5166628360748291},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4789167642593384},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4383787214756012},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4292992353439331},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3578492999076843},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0737677812576294},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10489-020-02019-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-020-02019-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-020-02019-1.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},{"id":"pmid:34764560","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34764560","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":"Applied intelligence (Dordrecht, Netherlands)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:7609830","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7609830","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"Appl Intell (Dordr)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1007/s10489-020-02019-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-020-02019-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-020-02019-1.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3096950781.pdf","grobid_xml":"https://content.openalex.org/works/W3096950781.grobid-xml"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W1517207589","https://openalex.org/W1904878066","https://openalex.org/W2018332268","https://openalex.org/W2037646098","https://openalex.org/W2039051707","https://openalex.org/W2093857038","https://openalex.org/W2099171060","https://openalex.org/W2107596396","https://openalex.org/W2613125831","https://openalex.org/W2740620254","https://openalex.org/W2756577788","https://openalex.org/W2765758918","https://openalex.org/W2783914690","https://openalex.org/W2792747889","https://openalex.org/W2913409447","https://openalex.org/W2919227859","https://openalex.org/W2945242125","https://openalex.org/W2963551763","https://openalex.org/W2963988212","https://openalex.org/W2988684285","https://openalex.org/W3001118548","https://openalex.org/W3001897055","https://openalex.org/W3002108456","https://openalex.org/W3002621688","https://openalex.org/W3003465021","https://openalex.org/W3003951199","https://openalex.org/W3004511262","https://openalex.org/W3004668429","https://openalex.org/W3005084260","https://openalex.org/W3005657121","https://openalex.org/W3005688502","https://openalex.org/W3006082171","https://openalex.org/W3006411443","https://openalex.org/W3006627382","https://openalex.org/W3007114958","https://openalex.org/W3007355693","https://openalex.org/W3007814559","https://openalex.org/W3008067660","https://openalex.org/W3008627141","https://openalex.org/W3008928918","https://openalex.org/W3008972896","https://openalex.org/W3010564092","https://openalex.org/W3010699833","https://openalex.org/W3011127849","https://openalex.org/W3011224398","https://openalex.org/W3011789411","https://openalex.org/W3013601031","https://openalex.org/W3016488464","https://openalex.org/W3017855299","https://openalex.org/W3018833108","https://openalex.org/W3024801014","https://openalex.org/W3025520558","https://openalex.org/W3026419502","https://openalex.org/W3030621456","https://openalex.org/W3032017599","https://openalex.org/W3033616466","https://openalex.org/W3033724106","https://openalex.org/W3034709671","https://openalex.org/W3036638392","https://openalex.org/W3038003980","https://openalex.org/W3041148517","https://openalex.org/W3045460727","https://openalex.org/W3046978353","https://openalex.org/W3049510520","https://openalex.org/W3092175256","https://openalex.org/W3125180703","https://openalex.org/W6946404314"],"related_works":["https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W2784060934","https://openalex.org/W2902714807","https://openalex.org/W2537489131","https://openalex.org/W2394084632","https://openalex.org/W2358293514","https://openalex.org/W2046633342","https://openalex.org/W2059273319","https://openalex.org/W2077021924"],"abstract_inverted_index":{"In":[0,36,100,203,340],"this":[1,209,341,356],"study,":[2,45,102,119,174,210,253,306],"which":[3,52,73],"aims":[4],"at":[5],"early":[6],"diagnosis":[7],"of":[8,27,51,72,117,172,198,244,251,256,304,324,347],"Covid-19":[9,55,76,93,330],"disease":[10],"using":[11,31,124,155,217],"X-ray":[12,49,70,126,167,326],"images,":[13,50,71,98,335],"the":[14,37,44,62,101,115,118,120,130,134,147,165,170,173,190,196,199,204,211,218,224,232,240,245,249,252,254,265,272,287,302,305,309,314,343],"deep-learning":[15],"approach,":[16],"a":[17,103,108,322],"state-of-the-art":[18],"artificial":[19],"intelligence":[20],"method,":[21],"was":[22,29,187,201,227],"used,":[23],"and":[24,56,77,95,107,133,195,234,262,282,298,313,316,332,353,369],"automatic":[25],"classification":[26,82],"images":[28,94,127,136,156,310,327,331],"performed":[30,247],"convolutional":[32],"neural":[33],"networks":[34],"(CNN).":[35],"first":[38,233,273],"training-test":[39,64,131,236,274,289,358],"data":[40,65,89,237,275,290,359],"set":[41,66,276,360],"used":[42],"in":[43,61,129,208],"there":[46,67],"were":[47,54,58,68,75,79,112,122,153,193,214,277,293,311,319,361],"230":[48],"150":[53,74,329],"80":[57],"non-Covid-19,":[59],"while":[60,285],"second":[63,235,288],"476":[69],"326":[78],"non-Covid-19.":[80],"Thus,":[81],"results":[83,121,192,243],"have":[84],"been":[85,182],"provided":[86],"for":[87,231,271,286,321,355],"two":[88],"sets,":[90],"containing":[91],"predominantly":[92,96],"non-Covid-19":[97,334],"respectively.":[99,300,371],"23-layer":[104],"CNN":[105,110],"architecture":[106,111],"54-layer":[109],"developed.":[113],"Within":[114,169,301],"scope":[116,171,250,303],"obtained":[123,137,157],"chest":[125,166],"directly":[128],"procedures":[132],"sub-band":[135],"by":[138,158,336],"applying":[139,159,337],"dual":[140],"tree":[141],"complex":[142],"wavelet":[143],"transform":[144],"(DT-CWT)":[145],"to":[146,164,363],"above-mentioned":[148],"images.":[149,168],"The":[150],"same":[151],"experiments":[152,205,246],"repeated":[154,320],"local":[160],"binary":[161],"pattern":[162],"(LBP)":[163],"four":[175],"new":[176],"result":[177],"generation":[178],"pipeline":[179],"algorithms":[180],"having":[181],"put":[183],"forward":[184],"additionally,":[185],"it":[186],"ensured":[188],"that":[189],"experimental":[191],"combined":[194,312],"success":[197],"study":[200],"improved.":[202],"carried":[206,215],"out":[207,216],"training":[212,315],"sessions":[213],"k-fold":[219],"cross":[220],"validation":[221],"method.":[222],"Here":[223],"k":[225],"value":[226],"chosen":[228],"as":[229],"23":[230],"sets.":[238],"Considering":[239],"average":[241,344],"highest":[242,345],"within":[248],"values":[255,346],"sensitivity,":[257,348],"specificity,":[258,349],"accuracy,":[259,350],"F-1":[260,351],"score,":[261,352],"area":[263],"under":[264],"receiver":[266],"operating":[267],"characteristic":[268],"curve":[269],"(AUC)":[270],"0,9947,":[278],"0,9800,":[279],"0,9843,":[280],"0,9881":[281],"0,9990":[283],"respectively;":[284],"set,":[291],"they":[292],"0,9920,":[294],"0,9939,":[295],"0,9891,":[296],"0,9828":[297],"0,9991;":[299],"finally,":[307],"all":[308],"testing":[317],"processes":[318],"total":[323],"556":[325],"comprising":[328],"406":[333],"2-fold":[338],"cross.":[339],"context,":[342],"AUC":[354],"last":[357],"found":[362],"be":[364],"0,9760,":[365],"1,0000,":[366],"0,9906,":[367],"0,9823":[368],"0,9997;":[370]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":9}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}