{"id":"https://openalex.org/W2799926361","doi":"https://doi.org/10.1117/12.2311282","title":"Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception","display_name":"Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception","publication_year":2018,"publication_date":"2018-04-13","ids":{"openalex":"https://openalex.org/W2799926361","doi":"https://doi.org/10.1117/12.2311282","mag":"2799926361"},"language":"en","primary_location":{"id":"doi:10.1117/12.2311282","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2311282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Tenth International Conference on Machine Vision (ICMV 2017)","raw_type":"proceedings-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/A5037047951","display_name":"Mehdi Habibzadeh Motlagh","orcid":null},"institutions":[{"id":"https://openalex.org/I202419968","display_name":"Royan Institute","ror":"https://ror.org/02exhb815","country_code":"IR","type":"nonprofit","lineage":["https://openalex.org/I202419968"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Mehdi Habibzadeh Motlagh","raw_affiliation_strings":["Royan Institute for Stem Cell Biology and Technology (Iran, Islamic Republic of)"],"affiliations":[{"raw_affiliation_string":"Royan Institute for Stem Cell Biology and Technology (Iran, Islamic Republic of)","institution_ids":["https://openalex.org/I202419968"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088979206","display_name":"Mahboobeh Jannesari","orcid":null},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mahboobeh Jannesari","raw_affiliation_strings":["Univ. of Tehran (Iran, Islamic Republic of)"],"affiliations":[{"raw_affiliation_string":"Univ. of Tehran (Iran, Islamic Republic of)","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079941148","display_name":"Zahra Rezaei","orcid":"https://orcid.org/0000-0003-4762-7903"},"institutions":[{"id":"https://openalex.org/I105818395","display_name":"University of Kashan","ror":"https://ror.org/015zmr509","country_code":"IR","type":"education","lineage":["https://openalex.org/I105818395"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Zahra Rezaei","raw_affiliation_strings":["Univ. of Kashan (Iran, Islamic Republic of)"],"affiliations":[{"raw_affiliation_string":"Univ. of Kashan (Iran, Islamic Republic of)","institution_ids":["https://openalex.org/I105818395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042903147","display_name":"Mehdi Totonchi","orcid":"https://orcid.org/0000-0002-7285-3155"},"institutions":[{"id":"https://openalex.org/I202419968","display_name":"Royan Institute","ror":"https://ror.org/02exhb815","country_code":"IR","type":"nonprofit","lineage":["https://openalex.org/I202419968"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mehdi Totonchi","raw_affiliation_strings":["Royan Institute for Stem Cell Biology and Technology (Iran, Islamic Republic of)"],"affiliations":[{"raw_affiliation_string":"Royan Institute for Stem Cell Biology and Technology (Iran, Islamic Republic of)","institution_ids":["https://openalex.org/I202419968"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091186278","display_name":"Hossein Baharvand","orcid":"https://orcid.org/0000-0001-6528-3687"},"institutions":[{"id":"https://openalex.org/I202419968","display_name":"Royan Institute","ror":"https://ror.org/02exhb815","country_code":"IR","type":"nonprofit","lineage":["https://openalex.org/I202419968"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Hossein Baharvand","raw_affiliation_strings":["Royan Institute for Stem Cell Biology and Technology (Iran, Islamic Republic of)"],"affiliations":[{"raw_affiliation_string":"Royan Institute for Stem Cell Biology and Technology (Iran, Islamic Republic of)","institution_ids":["https://openalex.org/I202419968"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5037047951"],"corresponding_institution_ids":["https://openalex.org/I202419968"],"apc_list":null,"apc_paid":null,"fwci":8.2964,"has_fulltext":false,"cited_by_count":195,"citation_normalized_percentile":{"value":0.98195101,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"105","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9865999817848206,"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":0.9797999858856201,"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/white-blood-cell","display_name":"White blood cell","score":0.6782127022743225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5845736861228943},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.533420741558075},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4498332440853119},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4424598217010498},{"id":"https://openalex.org/keywords/blood-cell","display_name":"Blood cell","score":0.42268311977386475},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.37439727783203125},{"id":"https://openalex.org/keywords/immunology","display_name":"Immunology","score":0.27745521068573}],"concepts":[{"id":"https://openalex.org/C2778488018","wikidata":"https://www.wikidata.org/wiki/Q42395","display_name":"White blood cell","level":2,"score":0.6782127022743225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5845736861228943},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.533420741558075},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4498332440853119},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4424598217010498},{"id":"https://openalex.org/C2779979121","wikidata":"https://www.wikidata.org/wiki/Q211709","display_name":"Blood cell","level":2,"score":0.42268311977386475},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.37439727783203125},{"id":"https://openalex.org/C203014093","wikidata":"https://www.wikidata.org/wiki/Q101929","display_name":"Immunology","level":1,"score":0.27745521068573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2311282","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2311282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Tenth International Conference on Machine Vision (ICMV 2017)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8100000023841858,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W82860031","https://openalex.org/W104184427","https://openalex.org/W107382872","https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1971819012","https://openalex.org/W1980276039","https://openalex.org/W2032536435","https://openalex.org/W2046224179","https://openalex.org/W2078488070","https://openalex.org/W2080860745","https://openalex.org/W2097117768","https://openalex.org/W2119703741","https://openalex.org/W2120420045","https://openalex.org/W2120708938","https://openalex.org/W2164777277","https://openalex.org/W2168894214","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2200094544","https://openalex.org/W2302517508","https://openalex.org/W2312404985","https://openalex.org/W2346062110","https://openalex.org/W2474421929","https://openalex.org/W2516161179","https://openalex.org/W2587265250","https://openalex.org/W2592678484","https://openalex.org/W2592888171","https://openalex.org/W2593345132","https://openalex.org/W2594392479","https://openalex.org/W2606416966","https://openalex.org/W2607075141","https://openalex.org/W2739179243","https://openalex.org/W2964350391","https://openalex.org/W4299518610","https://openalex.org/W6600284362","https://openalex.org/W6603386588","https://openalex.org/W6604254268","https://openalex.org/W6604269913","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6643348533","https://openalex.org/W6658231613","https://openalex.org/W6674914833","https://openalex.org/W6677872732","https://openalex.org/W6678065886","https://openalex.org/W6682132143","https://openalex.org/W6684665197","https://openalex.org/W6686164453","https://openalex.org/W6687483927","https://openalex.org/W6694260854","https://openalex.org/W6721136770","https://openalex.org/W6726299079","https://openalex.org/W6733090002","https://openalex.org/W6734090377","https://openalex.org/W6734147758","https://openalex.org/W6734195795","https://openalex.org/W6734570016","https://openalex.org/W6736430607"],"related_works":["https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W2601157893","https://openalex.org/W2373006798","https://openalex.org/W2131735617","https://openalex.org/W2056912418","https://openalex.org/W2033213769","https://openalex.org/W4312376745","https://openalex.org/W2136016640","https://openalex.org/W2049538278"],"abstract_inverted_index":{"This":[0,124],"works":[1],"gives":[2],"an":[3],"account":[4],"of":[5,7,32,61,102,108,155,166,179,184,254,337],"evaluation":[6,266],"white":[8,25,156,185,231,263,341],"blood":[9,26,62,85,89,157,186,232,264,294,342],"cell":[10,233,255,307,343],"differential":[11],"counts":[12],"via":[13],"computer":[14],"aided":[15],"diagnosis":[16,74],"(CAD)":[17],"system":[18],"and":[19,41,43,54,69,80,112,136,150,181,197,224,243,261,274,279,325,356,365,372,412,419],"hematology":[20],"rules.":[21],"Leukocytes,":[22,109],"also":[23,350],"called":[24],"cells":[27,187,265],"(WBCs)":[28],"play":[29],"main":[30,340],"role":[31],"the":[33,50,84,99,118,164,177,247,251,283,305,338,394],"immune":[34],"system.":[35],"Leukocyte":[36],"is":[37,64,93,318],"responsible":[38],"for":[39,77,148],"phagocytosis":[40],"immunity":[42],"therefore":[44],"in":[45,83,114,271,304,312,322,428],"defense":[46],"against":[47],"infection":[48],"involving":[49],"fatal":[51],"diseases":[52],"incidence":[53],"mortality":[55],"related":[56,110],"issues.":[57],"Admittedly,":[58],"microscopic":[59],"examination":[60,92],"samples":[63,104,260],"a":[65,144,200,319],"time":[66],"consuming,":[67],"expensive":[68],"error-prone":[70],"task.":[71],"A":[72],"manual":[73],"would":[75],"search":[76],"specific":[78],"Leukocytes":[79,429],"number":[81,101],"abnormalities":[82,168],"slides":[86],"while":[87,349],"complete":[88],"count":[90],"(CBC)":[91],"performed.":[94],"Complications":[95],"may":[96,160],"arise":[97],"from":[98,250],"large":[100],"varying":[103],"including":[105,193],"different":[106],"types":[107,192,344],"sub-types":[111],"concentration":[113],"blood,":[115],"which":[116,132,159],"makes":[117],"analysis":[119,253],"prone":[120],"to":[121,142,162],"human":[122],"error.":[123],"process":[125],"can":[126],"be":[127],"automated":[128],"by":[129],"computerized":[130],"techniques":[131],"are":[133,217,269],"more":[134],"reliable":[135],"economical.":[137],"In":[138,172,228,406],"essence,":[139],"we":[140,175],"seek":[141],"determine":[143],"fast,":[145],"accurate":[146],"mechanism":[147],"classification":[149,183,256,284],"gather":[151],"information":[152],"about":[153],"distribution":[154],"evidences":[158],"help":[161],"diagnose":[163],"degree":[165],"any":[167],"during":[169],"CBC":[170],"test.":[171],"this":[173,209,383],"work,":[174],"consider":[176],"problem":[178],"pre-processing":[180,214],"supervised":[182],"into":[188],"their":[189],"four":[190,339],"primary":[191],"Neutrophils,":[194],"Eosinophils,":[195],"Lymphocytes,":[196],"Monocytes":[198],"using":[199,241,276,323,345],"consecutive":[201,213],"proposed":[202,329,404],"deep":[203],"learning":[204],"framework.":[205],"For":[206],"first":[207],"step,":[208],"research":[210],"proposes":[211],"three":[212,403],"calculations":[215],"namely":[216],"color":[218],"distortion;":[219],"bounding":[220],"box":[221],"distortion":[222],"(crop)":[223],"image":[225],"flipping":[226],"mirroring.":[227],"second":[229],"phase,":[230],"recognition":[234],"performed":[235],"with":[236,257,282,288,328,354,361,369,423],"hierarchy":[237],"topological":[238],"feature":[239],"extraction":[240],"Inception":[242,324],"ResNet":[244,326,346,362,366],"architectures.":[245],"Finally,":[246],"results":[248],"obtained":[249,360],"preliminary":[252],"(11200)":[258],"training":[259],"1244":[262],"data":[267],"set":[268],"presented":[270],"confusion":[272,378],"matrices":[273],"interpreted":[275],"accuracy":[277,358],"rate,":[278],"false":[280,414,421],"positive":[281,422],"framework":[285,332],"being":[286],"validated":[287],"experiments":[289],"conducted":[290],"on":[291,334,402],"poor":[292],"quality":[293],"images":[295],"sized":[296],"320":[297],"\u00d7":[298],"240":[299],"pixels.":[300],"The":[301],"deferential":[302],"outcomes":[303],"challenging":[306],"detection":[308],"task,":[309],"as":[310],"shown":[311],"result":[313,353],"section,":[314],"indicate":[315],"that":[316,382],"there":[317],"significant":[320],"achievement":[321],"architecture":[327],"settings.":[330],"Our":[331],"detects":[333],"average":[335],"100%":[336],"V1":[347,363],"50":[348],"alternative":[351],"promising":[352],"99.84%":[355],"99.46%":[357],"rate":[359],"152":[364],"101,":[367],"respectively":[368],"3000":[370],"epochs":[371],"fine-tuning":[373],"all":[374],"layers.":[375],"Further":[376],"statistical":[377],"matrix":[379],"tests":[380],"revealed":[381],"work":[384,409],"achieved":[385],"1,":[386,399],"0.9979,":[387],"0.9989":[388],"sensitivity":[389],"values":[390,427],"when":[391],"area":[392],"under":[393],"curve":[395],"(AUC)":[396],"scores":[397],"above":[398],"0.9992,":[400],"0.9833":[401],"techniques.":[405],"addition,":[407],"current":[408],"shows":[410],"negligible":[411],"small":[413],"negative":[415],"0,":[416,424,425],"2,":[417],"1":[418],"substantial":[420],"5":[426],"detection.":[430]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":29},{"year":2022,"cited_by_count":45},{"year":2021,"cited_by_count":33},{"year":2020,"cited_by_count":31},{"year":2019,"cited_by_count":11},{"year":2018,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}