{"id":"https://openalex.org/W3215775222","doi":"https://doi.org/10.1080/21681163.2023.2219765","title":"Deep learning-based automated COVID-19 classification from computed tomography images","display_name":"Deep learning-based automated COVID-19 classification from computed tomography images","publication_year":2023,"publication_date":"2023-06-02","ids":{"openalex":"https://openalex.org/W3215775222","doi":"https://doi.org/10.1080/21681163.2023.2219765","mag":"3215775222"},"language":"en","primary_location":{"id":"doi:10.1080/21681163.2023.2219765","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2023.2219765","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2111.11191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020999017","display_name":"Kenan Morani","orcid":"https://orcid.org/0000-0002-4383-5732"},"institutions":[{"id":"https://openalex.org/I4387154896","display_name":"\u0130zmir Demokrasi \u00dcniversitesi","ror":"https://ror.org/04c152q53","country_code":null,"type":"education","lineage":["https://openalex.org/I4387154896"]},{"id":"https://openalex.org/I132257509","display_name":"Izmir University","ror":"https://ror.org/013h3xr51","country_code":"TR","type":"education","lineage":["https://openalex.org/I132257509"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Kenan Morani","raw_affiliation_strings":["Electrical and Electronics Engineering Department, Izmir Democracy University, Izmir, Turkey"],"affiliations":[{"raw_affiliation_string":"Electrical and Electronics Engineering Department, Izmir Democracy University, Izmir, Turkey","institution_ids":["https://openalex.org/I132257509","https://openalex.org/I4387154896"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006754641","display_name":"Devrim \u00dcnay","orcid":"https://orcid.org/0000-0003-3478-7318"},"institutions":[{"id":"https://openalex.org/I4387154896","display_name":"\u0130zmir Demokrasi \u00dcniversitesi","ror":"https://ror.org/04c152q53","country_code":null,"type":"education","lineage":["https://openalex.org/I4387154896"]},{"id":"https://openalex.org/I132257509","display_name":"Izmir University","ror":"https://ror.org/013h3xr51","country_code":"TR","type":"education","lineage":["https://openalex.org/I132257509"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"D. Unay","raw_affiliation_strings":["Electrical and Electronics Engineering Department, Izmir Democracy University, Izmir, Turkey"],"affiliations":[{"raw_affiliation_string":"Electrical and Electronics Engineering Department, Izmir Democracy University, Izmir, Turkey","institution_ids":["https://openalex.org/I132257509","https://openalex.org/I4387154896"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020999017"],"corresponding_institution_ids":["https://openalex.org/I132257509","https://openalex.org/I4387154896"],"apc_list":null,"apc_paid":null,"fwci":2.0867,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.86343967,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"11","issue":"6","first_page":"2145","last_page":"2160"},"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":0.989300012588501,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/computer-science","display_name":"Computer science","score":0.7680572271347046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7249273061752319},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6411312222480774},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5772994160652161},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49574336409568787},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.44908377528190613},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43455034494400024},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4253135919570923},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4125930368900299},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3385124206542969}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7680572271347046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7249273061752319},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6411312222480774},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5772994160652161},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49574336409568787},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.44908377528190613},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43455034494400024},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4253135919570923},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4125930368900299},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3385124206542969},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/21681163.2023.2219765","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2023.2219765","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2111.11191","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.11191","pdf_url":"https://arxiv.org/pdf/2111.11191","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2111.11191","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.11191","pdf_url":"https://arxiv.org/pdf/2111.11191","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1604924365","https://openalex.org/W2767195103","https://openalex.org/W2769215776","https://openalex.org/W2785809060","https://openalex.org/W2962858109","https://openalex.org/W2971923510","https://openalex.org/W3010496206","https://openalex.org/W3013174866","https://openalex.org/W3013633552","https://openalex.org/W3014524604","https://openalex.org/W3016653598","https://openalex.org/W3025953162","https://openalex.org/W3027763298","https://openalex.org/W3030621456","https://openalex.org/W3035825675","https://openalex.org/W3080758677","https://openalex.org/W3086650124","https://openalex.org/W3089290459","https://openalex.org/W3103635657","https://openalex.org/W3109022689","https://openalex.org/W3121891683","https://openalex.org/W3131326546","https://openalex.org/W3132764260","https://openalex.org/W3133765315","https://openalex.org/W3154692872","https://openalex.org/W3171916733","https://openalex.org/W3174065074","https://openalex.org/W3180389298","https://openalex.org/W3181966488","https://openalex.org/W3182345052","https://openalex.org/W3187743275","https://openalex.org/W3208040116","https://openalex.org/W3210205670","https://openalex.org/W3211058306","https://openalex.org/W3216781223","https://openalex.org/W4205249885","https://openalex.org/W4205761504","https://openalex.org/W4211225657","https://openalex.org/W4220782498","https://openalex.org/W4220804187","https://openalex.org/W4231910836","https://openalex.org/W4287101626","https://openalex.org/W4311219348","https://openalex.org/W6770844057"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3099765033","https://openalex.org/W3185156046","https://openalex.org/W2997155179","https://openalex.org/W2922305141"],"abstract_inverted_index":{"This":[0,77],"paper":[1],"introduces":[2],"a":[3,28,82,95,130,145,181],"lightweight":[4],"Convolutional":[5],"Neural":[6],"Networks":[7],"(CNN)":[8],"method":[9,148,161],"for":[10],"image":[11],"classification":[12,113],"in":[13,101,111,169],"COVID-19":[14],"diagnosis.":[15],"The":[16,32,47,104,159],"proposed":[17,105,160],"approach":[18,165],"emphasizes":[19],"simplicity":[20],"while":[21],"achieving":[22],"high":[23],"performance,":[24],"and":[25,43,67,93,129,166,180],"it":[26],"leverages":[27],"meticulously":[29],"annotated":[30,191],"database.":[31],"CNN":[33],"model":[34],"consists":[35],"of":[36,54,70,91,155,171],"four":[37],"convolutional":[38],"layers,":[39],"followed":[40],"by":[41,80,114,151],"flattening":[42],"two":[44],"dense":[45],"layers.":[46],"methodology":[48,106],"focuses":[49],"on":[50,98,176],"classifying":[51],"2D":[52],"slices":[53,63,72,154],"Computed":[55],"Tomography":[56],"(CT)":[57],"scans.":[58],"To":[59],"enhance":[60],"accuracy,":[61],"the":[62,68,74,88,139,153,163,177,189],"undergo":[64],"anatomy-relevant":[65],"masking":[66],"removal":[69],"non-representative":[71],"from":[73,188],"CT":[75,157],"volume.":[76],"is":[78,149],"achieved":[79],"cropping":[81],"fixed-sized":[83],"rectangular":[84],"area":[85],"to":[86,136],"capture":[87],"relevant":[89],"region":[90],"interest":[92],"using":[94],"threshold":[96],"based":[97],"bright":[99],"pixels":[100],"binarized":[102],"slices.":[103],"demonstrates":[107],"improved":[108],"quantitative":[109],"results":[110],"slice":[112,116,127,140],"employing":[115],"processing":[117],"techniques.":[118],"Additionally,":[119],"augmentation":[120],"techniques":[121],"such":[122],"as":[123],"class":[124],"weight":[125],"balancing,":[126],"flipping,":[128],"learning":[131],"rate":[132],"scheduler":[133],"are":[134],"applied":[135],"diagnose":[137],"at":[138],"level.":[141],"For":[142],"patient-level":[143],"diagnosis,":[144],"majority":[146],"voting":[147],"employed":[150],"considering":[152],"each":[156],"scan.":[158],"surpasses":[162],"baseline":[164],"other":[167],"alternatives":[168],"terms":[170],"macro":[172],"F1":[173],"score,":[174],"both":[175],"validation":[178],"set":[179],"test":[182],"partition":[183],"containing":[184],"previously":[185],"unseen":[186],"images":[187],"rigorously":[190],"dataset.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
