{"id":"https://openalex.org/W3170742935","doi":"https://doi.org/10.3906/elk-2009-1","title":"Determination of Pneumonia in X-ray Chest Images by Using Convolutional Neural Network","display_name":"Determination of Pneumonia in X-ray Chest Images by Using Convolutional Neural Network","publication_year":2020,"publication_date":"2020-11-25","ids":{"openalex":"https://openalex.org/W3170742935","doi":"https://doi.org/10.3906/elk-2009-1","mag":"3170742935"},"language":"en","primary_location":{"id":"doi:10.3906/elk-2009-1","is_oa":true,"landing_page_url":"https://doi.org/10.3906/elk-2009-1","pdf_url":null,"source":{"id":"https://openalex.org/S32837994","display_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES","issn_l":"1300-0632","issn":["1300-0632","1303-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310318422","host_organization_name":"Scientific and Technological Research Council of Turkey (TUBITAK)","host_organization_lineage":["https://openalex.org/P4310318422"],"host_organization_lineage_names":["Scientific and Technological Research Council of Turkey (TUBITAK)"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Turkish Journal of Electrical Engineering and Computer Sciences","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.3906/elk-2009-1","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076672955","display_name":"\u00d6zlem Polat","orcid":"https://orcid.org/0000-0002-9395-4465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00d6ZLEM POLAT","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Z\u00dcMRAY \u00d6LMEZ","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Z\u00dcMRAY \u00d6LMEZ","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006261253","display_name":"Tamer \u00d6lmez","orcid":"https://orcid.org/0000-0001-6124-2394"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"TAMER \u00d6LMEZ","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3524,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.66255757,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"29","issue":"3","first_page":"1615","last_page":"1627"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9994999766349792,"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":0.9994999766349792,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.984499990940094,"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":0.9653000235557556,"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/hyperparameter","display_name":"Hyperparameter","score":0.7934008836746216},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7632391452789307},{"id":"https://openalex.org/keywords/pneumonia","display_name":"Pneumonia","score":0.6505520343780518},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6162815690040588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5361167788505554},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5234543681144714},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5072466731071472},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4792613983154297},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.21225562691688538},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.10776206851005554},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.09912252426147461}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7934008836746216},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7632391452789307},{"id":"https://openalex.org/C2777914695","wikidata":"https://www.wikidata.org/wiki/Q12192","display_name":"Pneumonia","level":2,"score":0.6505520343780518},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6162815690040588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5361167788505554},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5234543681144714},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5072466731071472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4792613983154297},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21225562691688538},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.10776206851005554},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09912252426147461}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3906/elk-2009-1","is_oa":true,"landing_page_url":"https://doi.org/10.3906/elk-2009-1","pdf_url":null,"source":{"id":"https://openalex.org/S32837994","display_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES","issn_l":"1300-0632","issn":["1300-0632","1303-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310318422","host_organization_name":"Scientific and Technological Research Council of Turkey (TUBITAK)","host_organization_lineage":["https://openalex.org/P4310318422"],"host_organization_lineage_names":["Scientific and Technological Research Council of Turkey (TUBITAK)"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Turkish Journal of Electrical Engineering and Computer Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3906/elk-2009-1","is_oa":true,"landing_page_url":"https://doi.org/10.3906/elk-2009-1","pdf_url":null,"source":{"id":"https://openalex.org/S32837994","display_name":"TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES","issn_l":"1300-0632","issn":["1300-0632","1303-6203"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310318422","host_organization_name":"Scientific and Technological Research Council of Turkey (TUBITAK)","host_organization_lineage":["https://openalex.org/P4310318422"],"host_organization_lineage_names":["Scientific and Technological Research Council of Turkey (TUBITAK)"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Turkish Journal of Electrical Engineering and Computer Sciences","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Gender equality","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4206951940","https://openalex.org/W4293868382","https://openalex.org/W4382602594","https://openalex.org/W4387850423","https://openalex.org/W3202613528","https://openalex.org/W4312433101","https://openalex.org/W2993318283","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059"],"abstract_inverted_index":{"Pneumonia":[0],"is":[1,24,38,61,84,153,383],"one":[2],"of":[3,11,35,39,54,59,88,112,147,183,213,223,264,267,273,313,319,367],"the":[4,15,50,55,66,71,137,163,176,181,189,203,207,243,258,262,271,274,298,320,333,344,350,399],"major":[5,339],"diseases":[6],"that":[7,385],"cause":[8],"a":[9,78,98,148],"lot":[10],"deaths":[12],"all":[13],"over":[14],"world.":[16],"Determining":[17],"pneumonia":[18,37,60,89,111,122,366,378],"from":[19,90,139,236],"chest":[20],"X-ray":[21],"(CXR)":[22],"images":[23,235,269,303,327],"an":[25],"extremely":[26],"difficult":[27],"and":[28,57,69,97,117,142,169,186,220,246,305,323,372],"important":[29,48],"image":[30,308,322],"processing":[31],"problem.":[32],"The":[33,226,353],"discrimination":[34],"whether":[36],"bacterium":[40,113,368],"or":[41,108,114,120,123,289,362,369,376,379],"virus":[42,115,370],"origin":[43,58],"has":[44,356],"also":[45,187],"become":[46],"more":[47],"during":[49],"pandemic.":[51],"Automatic":[52],"determination":[53,212],"presence":[56],"crucial":[62],"for":[63,86,103,155,206,360,365,374],"speeding":[64],"up":[65],"treatment":[67],"process":[68],"increasing":[70],"patient's":[72],"survival":[73],"rate.":[74],"In":[75,126,335],"this":[76,127],"study,":[77],"convolutional":[79],"neural":[80,151],"network":[81,152,190,275],"(CNN)":[82],"framework":[83],"proposed":[85,227,351,354],"detection":[87],"CXR":[91,140,234,315],"images.":[92],"Two":[93],"different":[94,265],"binary":[95],"CNNs":[96,129],"triple":[99],"CNN":[100,229],"are":[101,130],"used":[102,185],"determining:":[104],"(\\textit":[105],"{i})":[106],"normal":[107,119,361,375],"pneumonia,":[109,363],"(\\textit{ii})":[110,215,278],"origin,":[116,371],"(\\textit{iii})":[118,221,311],"bacterial":[121,377],"viral":[124,380],"pneumonia.":[125,381],"approach,":[128],"trained":[131],"with":[132,159,194,391],"Walsh":[133,160],"functions":[134,161],"to":[135,166,172,191,202,233,238,256,287,291,297,332,398],"extract":[136],"features":[138],"images,":[141,299,316],"minimum":[143,177],"distance":[144,178],"classifier":[145,179],"instead":[146,312],"fully":[149],"connected":[150],"employed":[154],"classification":[156,208,218,259,345,387],"purpose.":[157],"Training":[158],"maintains":[162],"within-class":[164],"scattering":[165,171],"be":[167,173,192],"low,":[168],"between-class":[170],"high.":[174],"Preferring":[175],"reduces":[180],"number":[182],"nodes":[184],"allows":[188],"controlled":[193],"fewer":[195,224],"hyperparameters.":[196],"These":[197],"approaches":[198],"bring":[199],"some":[200],"advantages":[201],"system":[204],"designed":[205],"process:":[209],"(\\textit{i})":[210,261],"easy":[211],"hyperparameters,":[214],"achieving":[216],"higher":[217,386],"performance,":[219],"use":[222],"neurons.":[225],"small-size":[228],"model":[230],"was":[231,276,281],"applied":[232],"1-":[237],"5-year-old":[239],"children":[240],"provided":[241],"by":[242,283,293,300,306,348],"Guangzhou":[244],"Women's":[245],"Children's":[247],"Medical":[248],"Center":[249],"(GWCMC).":[250],"Three":[251],"experiments":[252],"have":[253],"been":[254],"conducted":[255],"improve":[257],"performance:":[260],"effect":[263],"sizes":[266],"input":[268,331],"on":[270],"performance":[272],"investigated,":[277],"training":[279],"set":[280],"augmented":[282],"randomly":[284,310],"flipping":[285],"left":[286],"right":[288],"down":[290],"up,":[292],"adding":[294],"Gaussian":[295],"noise":[296],"creating":[301],"negative":[302],"randomly,":[304],"changing":[307],"brightness":[309],"RGB":[314],"gray":[317],"component":[318],"original":[321],"four":[324],"2D":[325],"wavelet":[326],"were":[328,341,389],"given":[329],"as":[330],"network.":[334],"these":[336],"experiments,":[337],"no":[338],"changes":[340],"observed":[342,384],"in":[343],"results":[346],"obtained":[347,390],"using":[349],"CNNs.":[352],"method":[355],"achieved":[357],"100\\%":[358],"accuracy":[359],"92\\%":[364],"90\\%":[373],"It":[382],"performances":[388],"approximately":[392],"five":[393],"times":[394],"less":[395],"parameters":[396],"compared":[397]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
