{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:52:25Z","timestamp":1776329545307,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,16]]},"DOI":"10.7759\/cureus.107140","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T07:52:38Z","timestamp":1776325958000},"source":"Crossref","is-referenced-by-count":0,"title":["Automated Detection of COVID-19 Using Machine Learning Analysis of White Blood Cell Flow Cytometry Images"],"prefix":"10.7759","author":[{"given":"Akihiro E","family":"Hirosse","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9 R","family":"Backes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renata S","family":"Woloszynek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabio F","family":"Neves","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.55570","article-title":"Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19","volume":"9","author":"Gostic K","year":"2020","unstructured":"Gostic K, Gomez AC, Mummah RO, Kucharski AJ, Lloyd-Smith JO. Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19. Elife. 2020, 9:10.7554\/eLife.55570","journal-title":"Elife"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0234765","article-title":"The prevalence of symptoms in 24,410 adults infected by the novel coronavirus (SARS-CoV-2; COVID-19): a systematic review and meta-analysis of 148 studies from 9 countries","volume":"15","author":"Grant MC","year":"2020","unstructured":"Grant MC, Geoghegan L, Arbyn M, et al.. The prevalence of symptoms in 24,410 adults infected by the novel coronavirus (SARS-CoV-2; COVID-19): a systematic review and meta-analysis of 148 studies from 9 countries. PLoS One. 2020, 15:e0234765. 10.1371\/journal.pone.0234765","journal-title":"PLoS One"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhin.2023.07.028","article-title":"How have mathematical models contributed to understanding the transmission and control of SARS-CoV-2 in healthcare settings? A systematic search and review","volume":"141","author":"Smith DR","year":"2023","unstructured":"Smith DR, Chervet S, Pinettes T, et al.. How have mathematical models contributed to understanding the transmission and control of SARS-CoV-2 in healthcare settings? A systematic search and review. J Hosp Infect. 2023, 141:132-41. 10.1016\/j.jhin.2023.07.028","journal-title":"J Hosp Infect"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.41936","article-title":"Nosocomial SARS-CoV-2 infections and mortality during unique COVID-19 epidemic waves","volume":"6","author":"Dave N","year":"2023","unstructured":"Dave N, Sj\u00f6holm D, Hedberg P, et al.. Nosocomial SARS-CoV-2 infections and mortality during unique COVID-19 epidemic waves. JAMA Netw Open. 2023, 6:e2341936. 10.1001\/jamanetworkopen.2023.41936","journal-title":"JAMA Netw Open"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.beha.2022.101401","article-title":"COVID-19 disease and immune dysregulation","volume":"35","author":"Davitt E","year":"2022","unstructured":"Davitt E, Davitt C, Mazer MB, Areti SS, Hotchkiss RS, Remy KE. COVID-19 disease and immune dysregulation. Best Pract Res Clin Haematol. 2022, 35:101401. 10.1016\/j.beha.2022.101401","journal-title":"Best Pract Res Clin Haematol"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1515\/cclm-2022-0936","article-title":"Deciphering the role of monocyte and monocyte distribution width (MDW) in COVID-19: an updated systematic review and meta-analysis","volume":"61","author":"Ligi D","year":"2023","unstructured":"Ligi D, Lo Sasso B, Henry BM, et al.. Deciphering the role of monocyte and monocyte distribution width (MDW) in COVID-19: an updated systematic review and meta-analysis. Clin Chem Lab Med. 2023, 61:960-73. 10.1515\/cclm-2022-0936","journal-title":"Clin Chem Lab Med"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.intimp.2020.106705","article-title":"Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population","volume":"86","author":"Banerjee A","year":"2020","unstructured":"Banerjee A, Ray S, Vorselaars B, et al.. Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. Int Immunopharmacol. 2020, 86:106705. 10.1016\/j.intimp.2020.106705","journal-title":"Int Immunopharmacol"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/jcmm.17864","article-title":"A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters","volume":"27","author":"G\u00f3mez-Rojas S","year":"2023","unstructured":"G\u00f3mez-Rojas S, Segura GP, Oll\u00e9 J, et al.. A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters. J Cell Mol Med. 2023, 27:3423-30. 10.1111\/jcmm.17864","journal-title":"J Cell Mol Med"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13091549","article-title":"A systematic review and meta-analysis comparing the diagnostic accuracy tests of COVID-19","volume":"13","author":"Vilca-Alosilla JJ","year":"2023","unstructured":"Vilca-Alosilla JJ, Candia-Puma MA, Coronel-Monje K, et al.. A systematic review and meta-analysis comparing the diagnostic accuracy tests of COVID-19. Diagnostics (Basel). 2023, 13:10.3390\/diagnostics13091549","journal-title":"Diagnostics (Basel)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/34.574796","article-title":"Texture classification using windowed Fourier filters","volume":"19","author":"Azencott R","year":"1997","unstructured":"Azencott R, Wang JP, Younes L. Texture classification using windowed Fourier filters. IEEE Trans Pattern Anal Mach Intell. 1997, 19:148-53. 10.1109\/34.574796","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/34.531803","article-title":"Texture features for browsing and retrieval of image data","volume":"18","author":"Manjunath BS","year":"1996","unstructured":"Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell. 1996, 18:837-42. 10.1109\/34.531803","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/83.242353","article-title":"Texture analysis and classification with tree-structured wavelet transform","volume":"2","author":"Chang T","year":"1993","unstructured":"Chang T, Kuo CJ. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process. 1993, 2:429-41. 10.1109\/83.242353","journal-title":"IEEE Trans Image Process"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick RM","year":"1979","unstructured":"Haralick RM. Statistical and structural approaches to texture. Proc IEEE. 1979, 67:768-804. 10.1109\/PROC.1979.11328","journal-title":"Proc IEEE"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.1992.202065","article-title":"On local linear transform and Gabor filter representation of texture","volume":"3","author":"Ng I","year":"1992","unstructured":"Ng I, Tan T, Kittler J. On local linear transform and Gabor filter representation of texture. Int Con Pattern Recogn. 1992, 3:627-31. 10.1109\/ICPR.1992.202065","journal-title":"Int Con Pattern Recogn"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1976.5408777","article-title":"A comparative study of texture measures for terrain classification","volume":"6","author":"Weszka JS","year":"1976","unstructured":"Weszka JS, Dyer CR, Rosenfeld A. A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybern. 1976, 6:269-85. 10.1109\/TSMC.1976.5408777","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala T","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002, 24:971-87. 10.1109\/TPAMI.2002.1017623","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/BF02368424","article-title":"Fractal descriptions for spatial statistics","volume":"18","author":"King RB","year":"1990","unstructured":"King RB, Weissman LJ, Bassingthwaighte JB. Fractal descriptions for spatial statistics. Ann Biomed Eng. 1990, 18:111-21. 10.1007\/BF02368424","journal-title":"Ann Biomed Eng"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2013.05.008","article-title":"A new approach to estimate lacunarity of texture images","volume":"34","author":"Backes AR","year":"2013","unstructured":"Backes AR. A new approach to estimate lacunarity of texture images. Pattern Recognit Lett. 2013, 34:1455-61. 10.1016\/j.patrec.2013.05.008","journal-title":"Pattern Recognit Lett"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1080\/014311600750019985","article-title":"Test of a new lacunarity estimation method for image texture analysis","volume":"21","author":"Dong P","year":"2000","unstructured":"Dong P. Test of a new lacunarity estimation method for image texture analysis. Int J Remote Sens. 2000, 21:3369-73. 10.1080\/014311600750019985","journal-title":"Int J Remote Sens"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2014.205","article-title":"Adaptive median binary patterns for texture classification","author":"Hafiane A","year":"2014","unstructured":"Hafiane A, Palaniappan K, Seetharaman G. Adaptive median binary patterns for texture classification. 22nd International Conference on Pattern Recognition, Stockholm, Sweden. 2014, 1138-43. 10.1109\/ICPR.2014.205","journal-title":"22nd International Conference on Pattern Recognition, Stockholm, Sweden"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/213901","article-title":"Customized first and second order statistics based operators to support advanced texture analysis of MRI images","volume":"2013","author":"Avola D","year":"2013","unstructured":"Avola D, Cinque L, Placidi G. Customized first and second order statistics based operators to support advanced texture analysis of MRI images. Comput Math Methods Med. 2013, 2013:213901. 10.1155\/2013\/213901","journal-title":"Comput Math Methods Med"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2016.03.37","article-title":"Introduction to machine learning: k-nearest neighbors","volume":"4","author":"Zhang Z","year":"2016","unstructured":"Zhang Z. Introduction to machine learning: k-nearest neighbors. Ann Transl Med. 2016, 4:218. 10.21037\/atm.2016.03.37","journal-title":"Ann Transl Med"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher RA","year":"1936","unstructured":"Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen. 1936, 7:179-88. 10.1111\/j.1469-1809.1936.tb02137.x","journal-title":"Ann Eugen"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: a library for support vector machines","volume":"27","author":"Chang CC","year":"2011","unstructured":"Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011, 27:1-27. 10.1145\/1961189.1961199","journal-title":"ACM Trans Intell Syst Technol"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI55357.2022.9991771","article-title":"Pap-smear image classification by using a fusion of texture features","author":"Backes AR","year":"2022","unstructured":"Backes AR. Pap-smear image classification by using a fusion of texture features. 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). SIBGRAPI, Natal, Brazil; 2022. 139:144. 10.1109\/SIBGRAPI55357.2022.9991771"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1093\/clinchem\/hvaa200","article-title":"Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning","volume":"66","author":"Yang HS","year":"2020","unstructured":"Yang HS, Hou Y, Vasovic LV, et al.. Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. Clin Chem. 2020, 66:1396-404. 10.1093\/clinchem\/hvaa200","journal-title":"Clin Chem"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.2196\/24048","article-title":"Development and external validation of a machine learning tool to rule out COVID-19 among adults in the emergency department using routine blood tests: a large, multicenter, real-world study","volume":"22","author":"Plante TB","year":"2020","unstructured":"Plante TB, Blau AM, Berg AN, et al.. Development and external validation of a machine learning tool to rule out COVID-19 among adults in the emergency department using routine blood tests: a large, multicenter, real-world study. J Med Internet Res. 2020, 22:e24048. 10.2196\/24048","journal-title":"J Med Internet Res"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.13941","article-title":"Increased monocyte distribution width in COVID-19 and sepsis arises from a complex interplay of altered monocyte cellular size and subset frequency","volume":"44","author":"Cusinato M","year":"2022","unstructured":"Cusinato M, Hadcocks L, Yona S, Planche T, Macallan D. Increased monocyte distribution width in COVID-19 and sepsis arises from a complex interplay of altered monocyte cellular size and subset frequency. Int J Lab Hematol. 2022, 44:1029-39. 10.1111\/ijlh.13941","journal-title":"Int J Lab Hematol"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2004.07.008","article-title":"Texture analysis of medical images","volume":"59","author":"Castellano G","year":"2004","unstructured":"Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004, 59:1061-9. 10.1016\/j.crad.2004.07.008","journal-title":"Clin Radiol"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1093\/ckj\/sfaa188","article-title":"External validation of prognostic models: what,\u00a0why, how, when and where?","volume":"14","author":"Ramspek CL","year":"2021","unstructured":"Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: what,\u00a0why, how, when and where?. Clin Kidney J. 2021, 14:49-58. 10.1093\/ckj\/sfaa188","journal-title":"Clin Kidney J"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/475562-automated-detection-of-covid-19-using-machine-learning-analysis-of-white-blood-cell-flow-cytometry-images","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T07:52:39Z","timestamp":1776325959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/475562-automated-detection-of-covid-19-using-machine-learning-analysis-of-white-blood-cell-flow-cytometry-images"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,16]]},"references-count":30,"URL":"https:\/\/doi.org\/10.7759\/cureus.107140","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,16]]}}}