Готові списки джерел за темами / Blood cell counting

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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Blood cell counting"

1

Piacentini, Niccolò, Danilo Demarchi, Pierluigi Civera, and Marco Knaflitz. "Microsystems for Blood Cell Counting." Advances in Science and Technology 57 (September 2008): 55–60. http://dx.doi.org/10.4028/www.scientific.net/ast.57.55.

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This paper presents two biomedical microsystems for blood cell counting, designed and built through MultiMEMS Multi-Project Wafer (MPW) service and the microBUILDER European project. Dies mm in size, made of a micromachined glass-silicon-glass triple stack, host two new kinds of multiple micro-counters, suitable to investigate the feasibility of blood cell differential analysis by means of Coulter principle in a monolithic lab-on-a-chip, which integrates a microfluidic network, sensing metal electrodes and light-guiding structures. Within these devices, impedance method gains some innovative features, both from microsystem technology itself (low consumptions of chemicals, better analytical performances, low dead volumes in multifunctional interconnected networks, parallel high-throughput processing, low-cost mass production) and from new project solutions: self-aligning illumination allows to use compact external sources (i.e, LEDs) and requires no delicate optics. Different working set-ups (ranging from series with fixed control volume to parallel differential) can be achieved by adding only few external components. It is finally possible to combine electrical and optical measurements, oriented to multi-feature classification of cell sub-populations.
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2

Schmidt, R. M. "Automated differential blood cell counting systems." Clinical & Laboratory Haematology 1, no. 2 (June 28, 2008): 149–50. http://dx.doi.org/10.1111/j.1365-2257.1979.tb00463.x.

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3

Smith, Suzanne, Phophi Madzivhandila, René Sewart, Ureshnie Govender, Holger Becker, Pieter Roux, and Kevin Land. "Microfluidic Cartridges for Automated, Point-of-Care Blood Cell Counting." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 2 (November 19, 2016): 176–85. http://dx.doi.org/10.1177/2211068216677820.

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Disposable, low-cost microfluidic cartridges for automated blood cell counting applications are presented in this article. The need for point-of-care medical diagnostic tools is evident, particularly in low-resource and rural settings, and a full blood count is often the first step in patient diagnosis. Total white and red blood cell counts have been implemented toward a full blood count, using microfluidic cartridges with automated sample introduction and processing steps for visual microscopy cell counting to be performed. The functional steps within the microfluidic cartridge as well as the surrounding instrumentation required to control and test the cartridges in an automated fashion are described. The results recorded from 10 white blood cell and 10 red blood cell counting cartridges are presented and compare well with the results obtained from the accepted gold-standard flow cytometry method performed at pathology laboratories. Comparisons were also made using manual methods of blood cell counting using a hemocytometer, as well as a commercially available point-of-care white blood cell counting system. The functionality of the blood cell counting microfluidic cartridges can be extended to platelet counting and potential hemoglobin analysis, toward the implementation of an automated, point-of-care full blood count.
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4

Lewis, S. M., J. M. England, and F. Kubota. "Coincidence correction in red blood cell counting." Physics in Medicine and Biology 34, no. 9 (September 1, 1989): 1239–46. http://dx.doi.org/10.1088/0031-9155/34/9/009.

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5

Walberg, James. "White blood cell counting techniques in birds." Seminars in Avian and Exotic Pet Medicine 10, no. 2 (April 2001): 72–76. http://dx.doi.org/10.1053/saep.2001.22051.

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6

Fatichah, Chastine, Diana Purwitasari, Victor Hariadi, and Faried Effendy. "OVERLAPPING WHITE BLOOD CELL SEGMENTATION AND COUNTING ON MICROSCOPIC BLOOD CELL IMAGES." International Journal on Smart Sensing and Intelligent Systems 7, no. 3 (2014): 1271–86. http://dx.doi.org/10.21307/ijssis-2017-705.

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7

Chaturvedi, Shruti. "Counting the cost of caplacizumab." Blood 137, no. 7 (February 18, 2021): 871–72. http://dx.doi.org/10.1182/blood.2020009250.

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8

James L, Sherley, Daley Michael P, and Dutton Renly A. "Validation of Kinetic Stem Cell (KSC) counting algorithms for rapid quantification of human hematopoietic stem cells." Journal of Stem Cell Therapy and Transplantation 6, no. 1 (November 28, 2022): 029–37. http://dx.doi.org/10.29328/journal.jsctt.1001028.

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Specific quantification of therapeutic tissue stem cells (TSCs) is a major challenge. We recently described a computational simulation method for accurate and specific counting of TSCs. The method quantifies TSCs based on their unique asymmetric cell kinetics, which is rate-limiting for TSCs’ production of transiently-amplifying lineage-committed cells and terminally arrested cells during serial cell culture. Because of this basis, the new method is called kinetic stem cell (KSC) counting. Here, we report further validations of the specificity and clinical utility of KSC counting. First, we demonstrate its quantification of the expected increase in the hematopoietic stem cell (HSC) fraction of CD34+-selected preparations of human-mobilized peripheral blood cells, an approved treatment product routinely used for HSC transplantation therapies. Previously, we also used the KSC counting technology to define new mathematical algorithms with the potential for rapid determination of TSC-specific fractions without the need for serial culture. A second important HSC transplantation treatment, CD34+-selected umbilical cord blood (UCB) cells, was used to investigate this prediction. We show that, with an input of only simple population doubling time (PDT) data, the KSC counting-derived “Rabbit algorithms” can be used to rapidly determine the specific HSC fraction of CD34+-selected UCB cell preparations with a high degree of statistical confidence. The algorithms define the stem cell fraction half-life (SCFHL), a new parameter that projects stem cell numbers during expansion culture. These findings further validate KSC counting’s potential to meet the long-standing unmet need for a method to determine stem cell-specific dosage in stem cell medicine.
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H. Al-khafaji, Kawther, and Athraa H. Al-khafaji. "Diagnoses of Blood Disorder in Different Animal Species Depending on Counting Methods in Blood Cell Images." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 660. http://dx.doi.org/10.14419/ijet.v7i4.36.24218.

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Анотація:
Counting of red blood cells (RBCs) in microscope blood cell images, can give the pathologists valuable information regarding various hematological disorders, like anemia, leukemia,....etc. in several animal species, in this paper, an automated vision system has been developed which is capable of counting of red blood cells, in blood samples by applying different algorithms, based on red blood cellshape, the difference in the red blood cell shape of animal species make it difficult to use a one algorithm, therefore, for each animal species used specific algorithm which was capable of counting of RBCs effectively.
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MATSUNO, K., and 美恵 森本. "Peripheral Blood Cell Counting by Automated Hematology Analyzer." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 69, no. 1 (January 1, 1999): 25–29. http://dx.doi.org/10.4286/ikakikaigaku.69.1_25.

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Більше джерел

Дисертації з теми "Blood cell counting"

1

Li, Nan. "Lab-on-a-chip systems for blood cell separation, counting, and characterization." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872070051&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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2

Theera-Umpon, Nipon. "Morphological granulometric estimation with random primitives and applications to blood cell counting /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974689.

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3

Mauricio, Claudio Roberto Marquetto. "Contador de células vermelhas baseado em imagens para múltiplas espécies de animais silvestres e domésticos." Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2314.

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A contagem de células vermelhas do sangue desempenha um papel importante no diagnóstico de animais silvestres e domésticos. Apesar da existência de muitas tecnologias em diferentes contadores automatizados para análise de sangue, quando se trata do sangue de animais silvestres ainda é difícil encontrar uma solução simples e economicamente viável para múltiplas espécies. O objetivo deste estudo é desenvolver um contador automático de células vermelhas. Amostras de sangue (1 jaguatirica - Leopardus pardalis, 1 macaco - Cebus apella, 1 quati - Nasua nasua, 62 cães - Canis familiaris e 5 cavalos - Equus caballus) foram analisadas usando três métodos: 1-contagem manual, 2-contagem automática por imagem e 3-contagem semiautomática por imagem; as amostras de cães e cavalos foram analisadas por um quarto método: 4-contagem automática por impedância. As contagens dos métodos 2 e 3 foram obtidas usando o contador de células vermelhas proposto. Os resultados foram comparados usando a correlação de Pearson e gráficos com diferentes métodos como valor de referência. As contagens dos métodos 1, 2 e 3 correlacionaram muito bem com as contagens do método 4 (r ≥ 0.94). As contagens produzidas pelo método 2 apresentaram alta correlação com o método 3 (r = 0.998). Os resultados indicam que o contador proposto pode ser usado como um método de contagem automática ou semiautomática em clínicas que usam o método manual para contagem de células vermelhas do sangue de animais.
A RBC count plays an important role in the diagnostic of wild and domestic animals. Despite the many technologies available in different automated hematology analyzers, when it comes to blood of wild animals it is still difficult to find an easy and affordable solution for multiple species. This study aims to develop an automatic red blood cell counter. Blood samples (1 ocelot - Leopardus pardalis, 1 monkey - Cebus apella, 1 coati - Nasua nasua, 62 dogs - Canis familiaris and 5 horses - Equus caballus) were analyzed using three methods: 1-manual count, 2automatic count by image and 3-semi-automatic count by image; blood from dogs and horses were also analyzed by a fourth method: 4-automatic count by impedance. The counts of methods 2 and 3 were produced by the proposed red blood cell counter. Results were compared using Pearson’s correlation and plots with different methods as the criterion standard. RBC counts of methods 1, 2 and 3 correlated very well with those on the method 4 (r ≥ 0.94). RBC counts produced by method 2 were highly correlated with method 3 (r = 0.998). The results indicate that the proposed method can be used as an automatic or semi-automatic counting method in clinics that are currently using the manual method for RBC assessment.
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4

SILVA, NATAN V. da. "Produção e estudo de atividade antiangiogênica de proteínas de fusão endostatina-domínio BH3 das proteínas pró-apoptóticas PUMA e BIM." reponame:Repositório Institucional do IPEN, 2015. http://repositorio.ipen.br:8080/xmlui/handle/123456789/26940.

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Анотація:
Submitted by Marco Antonio Oliveira da Silva (maosilva@ipen.br) on 2016-12-22T11:54:03Z No. of bitstreams: 0
Made available in DSpace on 2016-12-22T11:54:03Z (GMT). No. of bitstreams: 0
A endostatina (ES) é uma proteína inibidora da angiogênese, com ação específica sobre células endoteliais em proliferação, utilizada para tratamento de tumores sólidos. No entanto, o elevado efeito antitumoral da ES observado em animais não é reproduzido em humanos. Com o intuito de potencializar a eficácia terapêutica da ES, produzimos duas proteínas híbridas com dois domínios funcionais. O primeiro domínio é a ES, que apresenta especificidade por células endoteliais ativadas, dirigindo estas proteínas de fusão às células endoteliais em proliferação, promovendo sua internalização e seu efeito inibitório. Como segundo domínio funcional utilizamos os domínios BH3 próapoptóticos de duas proteínas BH3-only com o objetivo de promover a liberação de citocromo C e desencadear o processo de apoptose, aumentando a ação antiangiogênica da ES. Neste trabalho, foram desenhadas duas proteínas de fusão que contêm o domínio BH3 das potentes proteínas pró apoptóticas PUMA e BIM (ES-PUMA e ES-BIM), que deveriam apresentar efeito antiangiogênico potencializado em relação à ES selvagem. A inserção dos fragmentos de DNA codificantes para os domínios BH3 de PUMA e BIM no vetor contendo o gene da ES (pET-ES) foram realizadas por mutagênese sítiodirigida. Estas proteínas de fusão recombinantes foram expressas como corpos de inclusão em E.coli, renaturadas utilizando processo que utiliza alta pressão e purificadas em resina de afinidade por heparina. O tratamento de células endoteliais com as proteínas ES-PUMA e ES-BIM não levou à queda de viabilidade em ensaio de MTS ou de apoptose avaliado por citometria de fluxo, em comparação com os resultados obtidos pelo tratamento com ES.
Dissertação (Mestrado em Tecnologia Nuclear)
IPEN/D
Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP
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5

Andrade, Hugo Miguel Felgueira de. "Image processing methodology for blood cell counting via mobile devices." Master's thesis, 2015. https://repositorio-aberto.up.pt/handle/10216/79416.

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Andrade, Hugo Miguel Felgueira de. "Image processing methodology for blood cell counting via mobile devices." Dissertação, 2015. https://repositorio-aberto.up.pt/handle/10216/79416.

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7

Yang, Sheng, and 楊昇. "Developing a microfluidic device with hydrodynamic trap arrays for white blood cell counting in peritoneal dialysis solution." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/64yxz9.

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Анотація:
碩士
國立臺灣大學
生醫電子與資訊學研究所
105
Peritoneal dialysis is a treatment for patients who suffer from severe chronic kidney disease. To prevent any infection during the treatment, it is important to monitor the population of white blood cell in peritoneal dialysis. At present, fluorescence-based flow cytometry and the automated hemocytometer are two prevailing methods to quantify the population of white blood cell. However, these techniques usually exist several limitations, such as (1) cannot deal with low white blood cell level (<300 cells/μL), (2) laborious assay preparation and manipulation steps. To address the above problems, we develop a microfluidic device with hydrodynamic trap arrays to capture white blood cells. The microfluidic microtrap array with multiple dimensions can trap general white blood cells and specific white blood cell subpopulation conjugated with 30μm polystyrene beads. This microfluidic platform enables simultaneously cell trapping, selection and can perform simple and real-time cell counting without complicated sample processing steps and equipment. To make the system more user-friendly and be suitable for point-of-care (POC) settings, we plan to make this microfluidic device compatible to existing smartphones and APP, which can perform image processing, analysis and data transmission. We believe this microfluidic platform for surveillance of white blood cell level will hold significant promise to provide the detailed infection status of patient for doctors to perform timely treatment.
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Liu, Hung-Chieh, and 劉宏傑. "An Improved Method For Counting Red Blood Cells." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/70376862824802462104.

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Анотація:
碩士
輔仁大學
資訊工程學系碩士班
103
Pulse coupled neural network (Pulse Coupled Neural Network, PCNN) is a new neural network evolved from traditional artificial neural network. and issued in1990 by Eckhornin accordance withthe sync pulse phenomenon of visual cortex of cats, monkeys and other animals.Related research and sustained developments have been widely used in image processing, target recognition, minimum path, and decision optimization. Image segmentation in the study of PCNN image processing technology, with similar neuronal input pulses occur simultaneously, for the image a little choppy and range obvious change inputs have a very effective remedy in order to retain a more complete picture regional information, which is the strength of image segmentation techniques, but when used PCNN image segmentation exist cycles of iterations can’t determine the problem.Through several experiments, PCNN image automatically wave propagation technology is found to be poor for segmentation of overlapped red blood cells and filter of small particles. In order to improve the above disadvantages, this paper proposes another way of thinking,PCNN edge detection technology can becombined with Hough circle detection.After conducting several experiments, results show that this method is superior to the original PCNN image segmentation.It can indeed achieve the separation of overlapped red blood cells and to filter out tiny particles.Soit is more in line with human eyes visually in counting the number of red blood cells.
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Книги з теми "Blood cell counting"

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McCann, Shaun R. The role of technology in haematology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198717607.003.0011.

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Various technologies have changed and improved the practice of haematology. Because it is possible to obtain blood cells so readily (via a simple venepuncture), haematology has been at the forefront of technological developments in medicine. The diagnosis of both malignant and benign haematological disorders has become more exact because of the technological advances outlined, and the understanding of the pathogenesis of many diseases has been advanced as a direct result of the application of technologies such as immunofluorescence, confocal and electron microscopy, automated cell counting, flow cytometry, digital cell morphology, advanced staining techniques, and PCR. However, it is important to stress that all technologies and ‘tests’ need to be cautiously interpreted, and a full history and physical examination should always be the first step in the investigation of patients.
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Частини книг з теми "Blood cell counting"

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Piacentini, Niccolò, Danilo Demarchi, Pierluigi Civera, and Marco Knaflitz. "Microsystems for Blood Cell Counting." In Advances in Science and Technology, 55–60. Stafa: Trans Tech Publications Ltd., 2008. http://dx.doi.org/10.4028/3-908158-14-1.55.

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2

Chaudhary, Ayesha Hoor, Javeria Ikhlaq, Muhammad Aksam Iftikhar, and Maham Alvi. "Blood Cell Counting and Segmentation Using Image Processing Techniques." In Applications of Intelligent Technologies in Healthcare, 87–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96139-2_9.

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Mohamed, Shahd T., Hala M. Ebeid, Aboul Ella Hassanien, and Mohamed F. Tolba. "Automatic White Blood Cell Counting Approach Based on Flower Pollination Optimization Multilevel Thresholoding Algorithm." In Advances in Intelligent Systems and Computing, 313–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_29.

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Ait Mehdi, Mohamed, Khadidja Belattar, and Feriel Souami. "An Enhanced Blood Cell Counting System Using Swin Transformer with Dynamic Head and KNN Model." In Communications in Computer and Information Science, 95–106. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4484-2_8.

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Huang, Luojie, Gregory N. McKay, and Nicholas J. Durr. "A Deep Learning Bidirectional Temporal Tracking Algorithm for Automated Blood Cell Counting from Non-invasive Capillaroscopy Videos." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 415–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_40.

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Chatterjee, Joydeep, Semanti Chakraborty, and Kanik Palodhi. "A Novel Automated Blood Cell Counting Method Based on Deconvolution and Convolution and Its Application to Neural Networks." In Advances in Intelligent Systems and Computing, 67–78. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2930-6_6.

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Mahanta, Lipi B., Kangkana Bora, Sourav Jyoti Kalita, and Priyangshu Yogi. "Automated Counting of Platelets and White Blood Cells from Blood Smear Images." In Lecture Notes in Computer Science, 13–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34872-4_2.

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Doering, Elena, Anna Pukropski, Ulf Krumnack, and Axel Schaffand. "Automatic Detection and Counting of Malaria Parasite-Infected Blood Cells." In Medical Imaging and Computer-Aided Diagnosis, 145–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5199-4_15.

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Palodhi, Kanik, Dhrubajyoti Dawn, and Amiya Halder. "Blood Cells Counting by Dynamic Area-Averaging Using Morphological Operations to SEM Images of Cancerous Blood Cells." In Advances in Intelligent Systems and Computing, 267–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28658-7_23.

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Rathore, Saima, Aksam Iftikhar, Ahmad Ali, Mutawarra Hussain, and Abdul Jalil. "Capture Largest Included Circles: An Approach for Counting Red Blood Cells." In Communications in Computer and Information Science, 373–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28962-0_36.

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Тези доповідей конференцій з теми "Blood cell counting"

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Piacentini, Niccolo, Danilo Demarchi, Pierluigi Civera, and Marco Knaflitz. "MEMS-based blood cell counting system." In 2008 15th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2008). IEEE, 2008. http://dx.doi.org/10.1109/icecs.2008.4674825.

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Nguyen, Viet Dung, Duy Hoang Ho, Viet Long Nguyen, and Ngoc Dung Bui. "Modified YOLOv5 for Blood Cell Counting." In 2022 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2022. http://dx.doi.org/10.1109/rivf55975.2022.10013896.

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Berge, Heidi, Dale Taylor, Sriram Krishnan, and Tania S. Douglas. "Improved red blood cell counting in thin blood smears." In 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872388.

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Tulsani, Hemant, Rashmi Gupta, and Rajiv Kapoor. "An improved methodology for blood cell counting." In 2013 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). IEEE, 2013. http://dx.doi.org/10.1109/mspct.2013.6782094.

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Su, Ting-Wei, Sungkyu Seo, Anthony Erlinger, and Aydogan Ozcan. "High-Throughput Cell Imaging, Counting and Characterization on a Chip." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-193255.

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Анотація:
We introduce a lensfree on chip imaging platform that enables high-throughput monitoring, counting, and identification of several different microscopic objects such as different cell types within a heterogeneous solution. This imaging platform can in principle be miniaturized to a hand-held device that can be used by minimally trained health care providers at the point-of-care to measure the cell count of e.g., red blood cells from whole blood samples with a counting speed of >100,000 cells/sec. This novel optical imaging platform can also be merged with microfluidic systems to be able to rapidly monitor and count hundreds of thousand of cells within a field-of-view (FOV) of ∼10 cm2 in vitro. The immediate impact of this lensfree on chip cell counting approach is its improved speed, significantly larger field-of-view and simplified design that permits considerable miniaturization of the entire cell counting device.
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Venkatalakshmi, B., and K. Thilagavathi. "Automatic red blood cell counting using hough transform." In 2013 IEEE Conference on Information & Communication Technologies (ICT). IEEE, 2013. http://dx.doi.org/10.1109/cict.2013.6558103.

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Narsale, Achal, Sakshi Nalwade, Medha Badgire, Sandhyarani Survase, and Chetan N. Aher. "Blood Cell Detection and Counting via Deep Learning." In 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). IEEE, 2022. http://dx.doi.org/10.1109/assic55218.2022.10088344.

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Mauricio, Claudio R. M., Fábio K. Schneider, and Leonilda Correia dos Santos. "Image-based red cell counting for wild animals blood." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5627383.

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Safuan, Syadia Nabilah Mohd, Razali Tomari, Wan Nurshazwani Wan Zakaria, and Nurmiza Othman. "White blood cell counting analysis of blood smear images using various segmentation strategies." In ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING: FROM THEORY TO APPLICATIONS: Proceedings of the International Conference on Electrical and Electronic Engineering (IC3E 2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5002036.

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Macawile, Merl James, Vonn Vincent Quinones, Alejandro Ballado, Jennifer Dela Cruz, and Meo Vincent Caya. "White blood cell classification and counting using convolutional neural network." In 2018 3rd International Conference on Control and Robotics Engineering (ICCRE). IEEE, 2018. http://dx.doi.org/10.1109/iccre.2018.8376476.

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