{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T19:57:00Z","timestamp":1777665420634,"version":"3.51.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16521-4","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T13:02:31Z","timestamp":1692968551000},"page":"25923-25949","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep U_ClusterNet: automatic deep clustering based segmentation and robust cell size determination in white blood cell"],"prefix":"10.1007","volume":"83","author":[{"given":"P R Krishna","family":"Prasad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edara Sreenivasa","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K Chandra","family":"Sekharaiah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"16521_CR1","doi-asserted-by":"publisher","first-page":"115441","DOI":"10.1016\/j.eswa.2021.115441","volume":"183","author":"MF Ab Aziz","year":"2021","unstructured":"Ab Aziz MF, Mostafa SA, Foozy CFM, Mohammed MA, Elhoseny M, Abualkishik AZ (2021) Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets. Expert Syst Appl 183:115441","journal-title":"Expert Syst Appl"},{"issue":"4","key":"16521_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-0912-y","volume":"42","author":"E Abdulhay","year":"2018","unstructured":"Abdulhay E, Mohammed MA, Ibrahim DA, Arunkumar N, Venkatraman V (2018) Computer aided solution for automatic segmenting and measurements of blood leucocytes using static microscope images. J Med Syst 42(4):1\u201312","journal-title":"J Med Syst"},{"issue":"9","key":"16521_CR3","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics11091295","volume":"11","author":"AA Abdulsahib","year":"2022","unstructured":"Abdulsahib AA, Mahmoud MA, Aris H, Gunasekaran SS, Mohammed MA (2022) An automated image segmentation and useful feature extraction algorithm for retinal blood vessels in fundus images. Electronics 11(9):1295","journal-title":"Electronics"},{"key":"16521_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13721-020-00274-3","volume":"10","author":"AA Abdulsahib","year":"2021","unstructured":"Abdulsahib AA, Mahmoud MA, Mohammed MA, Rasheed HH, Mostafa SA, Maashi MS (2021) Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images. Netw Model Anal Health Inf Bioinf 10:1\u201332","journal-title":"Netw Model Anal Health Inf Bioinf"},{"issue":"1","key":"16521_CR5","first-page":"115","volume":"3","author":"K Aggarwal","year":"2022","unstructured":"Aggarwal K, Mijwil MM, Al-Mistarehi AH, Alomari S, G\u00f6k M, Alaabdin AMZ, Abdulrhman SH (2022) Has the future started? The current growth of artificial intelligence, machine learning, and deep learning. Iraqi J Comput Sci Math 3(1):115\u2013123","journal-title":"Iraqi J Comput Sci Math"},{"key":"16521_CR6","first-page":"17","volume":"8","author":"KAK Al-Dulaimi","year":"2018","unstructured":"Al-Dulaimi KAK, Banks J, Chandran V, Tomeo-Reyes I, Nguyen Thanh K (2018) Classification of white blood cell types from microscope images: Techniques and challenges. Microsc Sci: Last App Educ Programs Appl Res 8:17\u201325","journal-title":"Microsc Sci: Last App Educ Programs Appl Res"},{"key":"16521_CR7","doi-asserted-by":"crossref","unstructured":"Ananthi VP, Thangaraj C, Easwaramoorthy D (2022) Multifractal dimensions and fractional differentiation in automated edge detection on intuitionistic fuzzy enhanced image. In: Frontiers of Fractal Analysis Recent Advances and Challenges CRC Press 153\u2013171","DOI":"10.1201\/9781003231202-8"},{"key":"16521_CR8","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, Kim KD (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":"16521_CR9","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1016\/j.ijleo.2018.05.011","volume":"168","author":"S Biswas","year":"2018","unstructured":"Biswas S, Hazra R (2018) Robust edge detection based on Modified Moore-Neighbor. Optik 168:931\u2013943","journal-title":"Optik"},{"key":"16521_CR10","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.measurement.2019.01.002","volume":"143","author":"D Gupta","year":"2019","unstructured":"Gupta D, Arora J, Agrawal U, Khanna A, de Albuquerque VHC (2019) Optimized binary bat algorithm for classification of white blood cells. Measurement 143:180\u2013190","journal-title":"Measurement"},{"key":"16521_CR11","doi-asserted-by":"crossref","unstructured":"Habibzadeh M, Jannesari M, Rezaei Z, Baharvand H, Totonchi M (2018) Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception. In: Tenth international conference on machine vision (ICMV 2017) International Society for Optics and Photonics, 10696: 1069612","DOI":"10.1117\/12.2311282"},{"key":"16521_CR12","unstructured":"Khamael AD, Al-Sabaawi A, Resen RD, Stephan JJ, Zwayen A (2019) Using adapted JSEG algorithm with fuzzy C mean for segmentation and counting of white blood cell and nucleus images. In: 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) 1\u20137"},{"key":"16521_CR13","doi-asserted-by":"crossref","unstructured":"King W, Toler K, Woodell-May J (2018) Role of white blood cells in blood-and bone marrow-based autologous therapies. BioMed Res Int 2018","DOI":"10.1155\/2018\/6510842"},{"key":"16521_CR14","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.micron.2011.03.009","volume":"42","author":"BC Ko","year":"2011","unstructured":"Ko BC, Gim JW, Nam JY (2011) Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 42:695\u2013705","journal-title":"Micron"},{"key":"16521_CR15","doi-asserted-by":"crossref","unstructured":"Kumar M, Jindal MK, Kumar M (2023) An efficient technique for breaking of coloured Hindi CAPTCHA. Soft Comput, 1\u201326","DOI":"10.1007\/s00500-023-07844-3"},{"issue":"4","key":"16521_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439798","volume":"20","author":"M Kumar","year":"2021","unstructured":"Kumar M, Jindal MK, Kumar M (2021) A novel attack on monochrome and greyscale Devanagari CAPTCHAs. Trans Asian Low-Res Language Inf Process 20(4):1\u201330","journal-title":"Trans Asian Low-Res Language Inf Process"},{"issue":"2","key":"16521_CR17","first-page":"1369","volume":"67","author":"CL Kumar","year":"2021","unstructured":"Kumar CL, Juliet AV, Ramakrishna B, Chakraborty S, Mohammed MA, Sunny KA (2021) Computational microfluidic channel for separation of escherichia coli from blood-cells. Comput Mater Contin 67(2):1369\u20131384","journal-title":"Comput Mater Contin"},{"key":"16521_CR18","doi-asserted-by":"crossref","unstructured":"Kumar PR, Sarkar A, Mohanty SN, Kumar PP (2020) Segmentation of white blood cells using image segmentation algorithms. In: 2020 5th International Conference on Computing, Communication and Security (ICCCS) IEEE 1\u20134","DOI":"10.1109\/ICCCS49678.2020.9277312"},{"key":"16521_CR19","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":"16521_CR20","doi-asserted-by":"publisher","first-page":"107006","DOI":"10.1016\/j.asoc.2020.107006","volume":"101","author":"Y Lu","year":"2021","unstructured":"Lu Y, Qin X, Fan H, Lai T, Li Z (2021) WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Appl Soft Comput 101:107006","journal-title":"Appl Soft Comput"},{"issue":"13","key":"16521_CR21","doi-asserted-by":"publisher","first-page":"17849","DOI":"10.1007\/s11042-022-12285-5","volume":"81","author":"M Makem","year":"2022","unstructured":"Makem M, Tiedeu A, Kom G, Nkandeu YPK (2022) A robust algorithm for white blood cell nuclei segmentation. Multimed Tools Appl 81(13):17849\u201317874","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"16521_CR22","doi-asserted-by":"publisher","first-page":"7011","DOI":"10.1007\/s11042-022-11939-8","volume":"81","author":"MM Mijwil","year":"2022","unstructured":"Mijwil MM, Aggarwal K (2022) A diagnostic testing for people with appendicitis using machine learning techniques. Multimed Tools Appl 81(5):7011\u20137023","journal-title":"Multimed Tools Appl"},{"key":"16521_CR23","doi-asserted-by":"crossref","unstructured":"Mohammadi E, Orooji M (2018) An unsupervised and supervised combined approach for white blood cells segmentation. In: 2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME) IEEE 1\u20136","DOI":"10.1109\/ICBME.2018.8703561"},{"issue":"8","key":"16521_CR24","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1002\/cyto.a.23794","volume":"95","author":"M Nassar","year":"2019","unstructured":"Nassar M, Doan M, Filby A, Wolkenhauer O, Fogg DK, Piasecka J, Thornton CA, Carpenter AE, Summers HD, Rees P, Hennig H (2019) Label-free identification of white blood cells using machine learning. Cytometry A 95(8):836\u2013842","journal-title":"Cytometry A"},{"key":"16521_CR25","doi-asserted-by":"crossref","unstructured":"Novoselnik F, Grbi\u0107 R, Gali\u0107 I, Dori\u0107 F (2018) Automatic white blood cell detection and identification using convolutional neural network. In: 2018 international conference on smart systems and technologies (SST) IEEE 163\u2013167","DOI":"10.1109\/SST.2018.8564625"},{"key":"16521_CR26","doi-asserted-by":"publisher","first-page":"7479","DOI":"10.1016\/j.eswa.2012.01.114","volume":"39","author":"C Pan","year":"2012","unstructured":"Pan C, Dong SP, Yoon S, Yang JC (2012) Leukocyte image segmentation using simulated visual attention. Expert Syst Appl 39:7479\u20137494","journal-title":"Expert Syst Appl"},{"key":"16521_CR27","doi-asserted-by":"crossref","unstructured":"Rajinikanth V, Kadry S, Dama\u0161evi\u010dius R, Sankaran D, Mohammed MA, Chander S (2022) Skin melanoma segmentation using VGG-UNet with Adam\/SGD optimizer: a study. In: 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), IEEE 982\u2013986","DOI":"10.1109\/ICICICT54557.2022.9917848"},{"key":"16521_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Int Conf Med Image Comput Comput-Assist Intervention 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"16521_CR29","doi-asserted-by":"publisher","first-page":"102385","DOI":"10.1016\/j.bspc.2020.102385","volume":"65","author":"RM Roy","year":"2021","unstructured":"Roy RM, Ameer PM (2021) Segmentation of leukocyte by semantic segmentation model: A deep learning approach. Biomed Signal Process Control 65:102385","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"16521_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-59215-9","volume":"10","author":"AT Sahlol","year":"2020","unstructured":"Sahlol AT, Kollmannsberger P, Ewees AA (2020) Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci Rep 10(1):1\u201311","journal-title":"Sci Rep"},{"key":"16521_CR31","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"},{"issue":"2","key":"16521_CR32","first-page":"3629","volume":"73","author":"A Sharma","year":"2022","unstructured":"Sharma A, Prashar D, Khan AA, Khan FA, Poochaya S (2022) Automatic leukaemia segmentation approach for blood cancer classification using microscopic images. Comput Mat Contin 73(2):3629\u20133648","journal-title":"Comput Mat Contin"},{"issue":"1","key":"16521_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-98599-0","volume":"11","author":"S Tavakoli","year":"2021","unstructured":"Tavakoli S, Ghaffari A, Kouzehkanan ZM, Hosseini R (2021) New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images. Sci Rep 11(1):1\u201313","journal-title":"Sci Rep"},{"key":"16521_CR34","doi-asserted-by":"crossref","unstructured":"Theera-Umpon N (2005) White blood cell segmentation and classification in microscopic bone marrow images. Int Conf Fuzzy Syst Knowledge Discov 787\u2013796","DOI":"10.1007\/11540007_98"},{"key":"16521_CR35","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"},{"issue":"1\u20132","key":"16521_CR36","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1504\/IJCC.2021.113974","volume":"10","author":"D Umamaheswari","year":"2021","unstructured":"Umamaheswari D, Geetha S (2021) Fuzzy-C means segmentation of lymphocytes for the identification of the differential counting of WBC. Int J Cloud Comput 10(1\u20132):26\u201342","journal-title":"Int J Cloud Comput"},{"issue":"6","key":"16521_CR37","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1007\/s11263-021-01449-9","volume":"129","author":"H Yan","year":"2021","unstructured":"Yan H, Mao X, Yang X, Xia Y, Wang C, Wang J, Xia R, Xu X, Wang Z, Li Z, Zhao X (2021) Development and validation of an unsupervised feature learning system for leukocyte characterization and classification: a multi-hospital study. Int J Comput Vision 129(6):1837\u20131856","journal-title":"Int J Comput Vision"},{"issue":"3","key":"16521_CR38","doi-asserted-by":"publisher","first-page":"036001","DOI":"10.1117\/1.JBO.26.3.036001","volume":"26","author":"F Yi","year":"2021","unstructured":"Yi F, Park S, Moon I (2021) High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks. J Biomed Opt 26(3):036001","journal-title":"J Biomed Opt"},{"key":"16521_CR39","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.micron.2013.09.006","volume":"56","author":"X Zheng","year":"2014","unstructured":"Zheng X, Wang Y, Wang G, Chen Z (2014) A novel algorithm based on visual saliency attention for localization and segmentation in rapidly-stained leukocyte images. Micron 56:17\u201328","journal-title":"Micron"},{"key":"16521_CR40","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.micron.2018.01.010","volume":"107","author":"X Zheng","year":"2018","unstructured":"Zheng X, Wang Y, Wang G, Liu J (2018) Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 107:55\u201371","journal-title":"Micron"},{"key":"16521_CR41","doi-asserted-by":"crossref","unstructured":"Zhong Y, Huang M, Fan H, Hu R, Li Z (2021) An improved unsupervised white blood cell classification via contrastive learning. In: International Conference on Data Mining and Big Data Springer, Singapore 100\u2013109","DOI":"10.1007\/978-981-16-7476-1_10"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16521-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16521-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16521-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T10:37:25Z","timestamp":1709203045000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16521-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,25]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16521"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16521-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,25]]},"assertion":[{"value":"18 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors have agreed to participate in this submitted article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All the authors involved in this manuscript give full consent to publish this submitted article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}