{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T01:32:49Z","timestamp":1777599169627,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s11042-022-12539-2","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T14:33:49Z","timestamp":1649169229000},"page":"30121-30142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Automatic classification of white blood cells using deep features based convolutional neural network"],"prefix":"10.1007","volume":"81","author":[{"given":"A.","family":"Meenakshi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. Anitha","family":"Ruth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. R.","family":"Kanagavalli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Uma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"12539_CR1","doi-asserted-by":"crossref","unstructured":"Achmad Abdurrazzaq AK, Junoh Z, Yahya, Mohd I (2020) New white blood cell detection technique by using singular value decomposition concept. Multimed Tools Appl 80(3):4627\u20134638","DOI":"10.1007\/s11042-020-09946-8"},{"key":"12539_CR2","doi-asserted-by":"publisher","first-page":"102031","DOI":"10.1016\/j.simpat.2019.102031","volume":"101","author":"M Almiani","year":"2020","unstructured":"Almiani M, AbuGhazleh A, Al-Rahayfeh A, Atiewi S, Razaque A (2020) Deep recurrent neural network for IoT intrusion detection system. Simul Model Pract Theory 101:102031","journal-title":"Simul Model Pract Theory"},{"key":"12539_CR3","unstructured":"Bani-Hani D, Khan N, Alsultan F, Karanjkar S, Nagarur N (2018) Classification of leucocytes using convolutional neural network optimized through genetic algorithm"},{"key":"12539_CR4","doi-asserted-by":"publisher","first-page":"113211","DOI":"10.1016\/j.eswa.2020.113211","volume":"149","author":"PP Banik","year":"2020","unstructured":"Banik PP, Saha R, Ki-Doo K (2020) An automatic nucleus segmentation and CNN model based classification method of white blood cell. Expert Syst Appl 149:113211","journal-title":"Expert Syst Appl"},{"key":"12539_CR5","doi-asserted-by":"publisher","first-page":"195929","DOI":"10.1109\/ACCESS.2020.3031718","volume":"8","author":"T Bhattacharyya","year":"2020","unstructured":"Bhattacharyya T, Chatterjee B, Singh PK, Yoon JH, Geem ZW, Sarkar R (2020) Mayfly in harmony: A new hybrid meta-heuristic feature selection algorithm. IEEE Access 8:195929\u2013195945","journal-title":"IEEE Access"},{"key":"12539_CR6","first-page":"403","volume-title":"Innovations in Computational Intelligence and Computer Vision","author":"K Bhoopathy","year":"2021","unstructured":"Bhoopathy K (2021) Computer assisted classification framework for detection of acute myeloid leukemia in peripheral blood smear images. Innovations in Computational Intelligence and Computer Vision. Springer, Singapore, pp 403\u2013410"},{"issue":"1","key":"12539_CR7","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1111\/ejh.13516","volume":"106","author":"V Buxhofer-Ausch","year":"2021","unstructured":"Buxhofer-Ausch V, Wolf D, Sormann S, Forjan E, Schimetta W, Gisslinger B, Heibl S et al (2021) Impact of platelets on major thrombosis in patients with a normal white blood cell count in essential thrombocythemia. Eur J Haematol 106(1):58\u201363","journal-title":"Eur J Haematol"},{"key":"12539_CR8","doi-asserted-by":"publisher","first-page":"142521","DOI":"10.1109\/ACCESS.2020.3012292","volume":"8","author":"K Deepika","year":"2020","unstructured":"Deepika K, Jain N, Khurana A, Mittal S, Satapathy SC, Senkerik R, Hemanth JD (2020) Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access 8:142521\u2013142531","journal-title":"IEEE Access"},{"issue":"2","key":"12539_CR9","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654\u2013669","journal-title":"Eur J Oper Res"},{"key":"12539_CR10","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-642-38610-7_25","volume-title":"Artif Intell Soft Comput","author":"M Habibzadeh","year":"2013","unstructured":"Habibzadeh M, Krzy\u017cak A, Fevens T (2013) White blood cell differential counts using convolutional neural networks for low resolution images. In: Rutkowski L, Korytkowski M, Tadeusiewicz RSR, Zadeh LA, Zurada JM (eds) Artif Intell Soft Comput. Springer, Berlin Heidelberg, pp 263\u2013274"},{"issue":"9","key":"12539_CR11","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1109\/JBHI.2020.2970091","volume":"24","author":"J He","year":"2020","unstructured":"He J, Wang C, Jiang D, Li Z, Liu Y, Zhang T (2020) CycleGAN with an improved loss function for cell detection using partly labeled images. IEEE J Biomed Health Inf 24(9):2473\u20132480","journal-title":"IEEE J Biomed Health Inf"},{"issue":"13","key":"12539_CR12","doi-asserted-by":"publisher","first-page":"17879","DOI":"10.1007\/s11042-018-7107-x","volume":"78","author":"RB Hegde","year":"2019","unstructured":"Hegde RB, Prasad K, Hebbar H, Singh BMK (2019) Development of a robust algorithm for detection of nuclei of white blood cells in peripheral blood smear images. Multimed Tools Appl 78(13):17879\u201317898","journal-title":"Multimed Tools Appl"},{"key":"12539_CR13","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/s13246-019-00742-9","volume":"42","author":"RB Hegde","year":"2019","unstructured":"Hegde RB, Prasad K, Hebbar H, Singh BMK (2019) Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study. Australas Phys Eng Sci Med 42:627\u2013638","journal-title":"Australas Phys Eng Sci Med"},{"key":"12539_CR14","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/app8081325","volume":"8","author":"MF Ijaz","year":"2018","unstructured":"Ijaz MF, Alfian G, Syafrudin M, Rhee J (2018) Hybrid prediction model for type 2 diabetes and hypertension using DBSCAN-based outlier detection, synthetic minority over sampling technique (SMOTE), and random forest. Appl Sci 8:8","journal-title":"Appl Sci"},{"issue":"10","key":"12539_CR15","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.3390\/s20102809","volume":"20","author":"MF Ijaz","year":"2020","unstructured":"Ijaz MF, Attique M, Son Y (2020)Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors 20(10):2809","journal-title":"Sensors"},{"issue":"2","key":"12539_CR16","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1002\/jemt.23578","volume":"84","author":"MA Khan","year":"2021","unstructured":"Khan MA, Qasim M, Lodhi HMJ, Nazir M, Javed K, Rubab S, Din A, Habib U (2021) Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM. Microsc Res Tech 84(2):202\u2013216","journal-title":"Microsc Res Tech"},{"key":"12539_CR17","doi-asserted-by":"publisher","first-page":"109472","DOI":"10.1016\/j.mehy.2019.109472","volume":"135","author":"H Kutlu","year":"2020","unstructured":"Kutlu H, Avci E, \u00d6zyurt F (2020) White blood cells detection and classification based on regional convolutional neural networks. Med Hypotheses 135:109472","journal-title":"Med Hypotheses"},{"key":"12539_CR18","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.envpol.2017.08.114","volume":"231","author":"X Li","year":"2017","unstructured":"Li X, Peng L, Yao X, Cui S, Hu Y, You C, Chi T (2017) Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. Environ Pollut 231:997\u20131004","journal-title":"Environ Pollut"},{"key":"12539_CR19","doi-asserted-by":"publisher","first-page":"36188","DOI":"10.1109\/ACCESS.2018.2846685","volume":"6","author":"G Liang","year":"2018","unstructured":"Liang G, Hong H, Xie W, Zheng L (2018) Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access 6:36188\u201336197","journal-title":"IEEE Access"},{"key":"12539_CR20","doi-asserted-by":"publisher","first-page":"106548","DOI":"10.1016\/j.knosys.2020.106548","volume":"212","author":"JC-W Lin","year":"2021","unstructured":"Lin JC-W, Shao Y, Djenouri Y, Yun U (2021) ASRNN: a recurrent neural network with an attention model for sequence labeling. Knowl Based Syst 212:106548","journal-title":"Knowl Based Syst"},{"key":"12539_CR21","doi-asserted-by":"publisher","first-page":"100416","DOI":"10.1016\/j.imu.2020.100416","volume":"20","author":"M Makem","year":"2020","unstructured":"Makem M, Tiedeu A (2020) An efficient algorithm for detection of white blood cell nuclei using adaptive three stage PCA-based fusion. Inf Med Unlocked 20:100416","journal-title":"Inf Med Unlocked"},{"key":"12539_CR22","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3390\/s21165571","volume":"21","author":"M Mandal","year":"2021","unstructured":"Mandal M, Singh PK, Ijaz MF, Shafi J, Sarkar R (2021) A tri-stage wrapper-filter feature selection framework for disease classification. Sensors 21:16","journal-title":"Sensors"},{"key":"12539_CR23","doi-asserted-by":"crossref","unstructured":"Monteiro A, Carolina B, Iano Y, Fran\u00e7a RP (2017) Detecting and counting of blood cells using watershed transform: an improved methodology. In: Brazilian Technology Symposium. Springer, Cham, pp 301\u2013310","DOI":"10.1007\/978-3-319-93112-8_31"},{"key":"12539_CR24","doi-asserted-by":"publisher","first-page":"103530","DOI":"10.1016\/j.compbiomed.2019.103530","volume":"116","author":"CD Ruberto","year":"2020","unstructured":"Ruberto CD, Loddo A, Putzu L (2020) Detection of red and white blood cells from microscopic blood images using a region proposal approach. Comput Biol Med 116:103530","journal-title":"Comput Biol Med"},{"key":"12539_CR25","doi-asserted-by":"crossref","unstructured":"Qian R, McNabb RP, Zhou KC, Mousa HM, Saban DR, Perez VL, Kuo AN, Izatt JA (2021) In vivo quantitative analysis of anterior chamber white blood cell mixture composition using spectroscopic optical coherence tomography. arXiv preprint arXiv:2101.03719","DOI":"10.1364\/BOE.419063"},{"key":"12539_CR26","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.matcom.2020.08.013","volume":"179","author":"D Sedighizadeh","year":"2021","unstructured":"Sedighizadeh D, Masehian E, Sedighizadeh M, Akbaripour H (2021) GEPSO: A new generalized particle swarm optimization algorithm. Math Comput Simul 179:194\u2013212","journal-title":"Math Comput Simul"},{"key":"12539_CR27","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.cmpb.2017.11.015","volume":"168","author":"AI Shahin","year":"2019","unstructured":"Shahin AI, Guo Y, Amin KM, Sharawi AA (2019) White blood cells identification system based on convolutional deep neural learning networks. Comput Methods Programs Biomed 168:69\u201380","journal-title":"Comput Methods Programs Biomed"},{"key":"12539_CR28","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-981-13-3600-3_13","volume-title":"Soft Comput Signal Process","author":"M Sharma","year":"2019","unstructured":"Sharma M, Bhave A, Janghel RR (2019) White blood cell classification using convolutional neural network. In: Wang J, Reddy GRM, Prasad VK, Reddy VS (eds) Soft Comput Signal Process. Springer, Singapore, pp 135\u2013143"},{"issue":"8","key":"12539_CR29","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.3390\/s21082852","volume":"21","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ (2021) Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21(8):2852","journal-title":"Sensors"},{"issue":"3","key":"12539_CR30","doi-asserted-by":"publisher","first-page":"3303","DOI":"10.32604\/cmc.2021.018472","volume":"69","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu PN, Ahmed S, Alhumam A, Kumar AB, Muhammad Fazal I (2021) An AW-HARIS based automated segmentation of human liver using CT images. CMC-Comput Mater Continua 69(3):3303\u20133319","journal-title":"CMC-Comput Mater Continua"},{"issue":"1","key":"12539_CR31","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1093\/annonc\/mdx636","volume":"29","author":"MIlieE Szafer-Glusman","year":"2018","unstructured":"Szafer-Glusman MIlieE, Hofman V, Chamorey E, Lalv\u00e9e S, Selva E, Leroy S et al (2018) Detection of PD-L1 in circulating tumor cells and white blood cells from patients with advanced non-small-cell lung cancer. Ann Oncol 29(1):193\u2013199","journal-title":"Ann Oncol"},{"key":"12539_CR32","doi-asserted-by":"publisher","first-page":"106810","DOI":"10.1016\/j.asoc.2020.106810","volume":"97","author":"M To\u011fa\u00e7ar","year":"2020","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z (2020) Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods. Appl Soft Comput 97:106810","journal-title":"Appl Soft Comput"},{"key":"12539_CR33","doi-asserted-by":"publisher","unstructured":"Wang JL, Li AY, Huang M, Ibrahim AK, Zhuang H, Ali AM (2018) Classification of white blood cells with PatternNet-fused Ensemble of Convolutional Neural Networks (PECNN), In: 2018 IEEE Int Symp Signal Process Inf Technol, pp 325\u2013330. https:\/\/doi.org\/10.1109\/ISSPIT.2018.8642630","DOI":"10.1109\/ISSPIT.2018.8642630"},{"key":"12539_CR34","doi-asserted-by":"publisher","first-page":"100808","DOI":"10.1016\/j.swevo.2020.100808","volume":"60","author":"F Wang","year":"2021","unstructured":"Wang F, Zhang H, Zhou A (2021) A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm Evol Comput 60:100808","journal-title":"Swarm Evol Comput"},{"key":"12539_CR35","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.micron.2018.01.010","volume":"107","author":"Y Xin Zheng","year":"2018","unstructured":"Xin Zheng Y, Wang G, Wang Liu J (2018) Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 107:55\u201371","journal-title":"Micron"},{"issue":"1","key":"12539_CR36","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1080\/21691401.2021.1879823","volume":"49","author":"X Yao","year":"2021","unstructured":"Yao X, Sun K, Bu X, Zhao C (2021) Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artif Cells Nanomed Biotechnol 49(1):147\u2013155","journal-title":"Artif Cells Nanomed Biotechnol"},{"key":"12539_CR37","doi-asserted-by":"publisher","first-page":"106559","DOI":"10.1016\/j.cie.2020.106559","volume":"145","author":"K Zervoudakis","year":"2020","unstructured":"Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:106559","journal-title":"Comput Ind Eng"},{"key":"12539_CR38","doi-asserted-by":"crossref","unstructured":"Zhao J, Gao Z-M (2020) The improved mayfly optimization algorithm with Chebyshev map. J Phys Conf Ser 1684(1):012075. IOP Publishing","DOI":"10.1088\/1742-6596\/1684\/1\/012075"},{"key":"12539_CR39","doi-asserted-by":"crossref","unstructured":"Zhao J, Gao Z-M(2020) The negative mayfly optimization algorithm. J Phys Conf Ser 1693(1):012098. IOP Publishing","DOI":"10.1088\/1742-6596\/1693\/1\/012098"},{"issue":"8","key":"12539_CR40","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1007\/s11517-016-1590-x","volume":"55","author":"J Zhao","year":"2017","unstructured":"Zhao J, Zhang M, Zhou Z, Chu J, Cao F (2017) Automatic detection and classification of leukocytes using convolutional neural networks. Med Biol Eng Comput 55(8):1287\u20131301","journal-title":"Med Biol Eng Comput"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12539-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12539-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12539-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T05:29:09Z","timestamp":1660714149000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12539-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,5]]},"references-count":40,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["12539"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12539-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,5]]},"assertion":[{"value":"22 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}