<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2215-3470</journal-id>
<journal-title><![CDATA[Uniciencia]]></journal-title>
<abbrev-journal-title><![CDATA[Uniciencia]]></abbrev-journal-title>
<issn>2215-3470</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional, Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2215-34702022000100449</article-id>
<article-id pub-id-type="doi">10.15359/ru.36-1.28</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[An efficient convolutional neural network to detect and count blood cells]]></article-title>
<article-title xml:lang="es"><![CDATA[Eficiente red neuronal convolucional para detectar y contar los glóbulos de la sangre]]></article-title>
<article-title xml:lang="pt"><![CDATA[Rede neural convolucional eficiente para detecção e contagem dos glóbulos sanguíneos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chandra Joshi]]></surname>
<given-names><![CDATA[Rakesh]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Yadav]]></surname>
<given-names><![CDATA[Saumya]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Kishore Dutta]]></surname>
<given-names><![CDATA[Malay]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Travieso-Gonzalez]]></surname>
<given-names><![CDATA[Carlos M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Dr. A.P.J. Abdul Kalam Technical University Centre for Advanced Studies ]]></institution>
<addr-line><![CDATA[Lucknow U.P.]]></addr-line>
<country>India</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Dr. A.P.J. Abdul Kalam Technical University Centre for Advanced Studies ]]></institution>
<addr-line><![CDATA[Lucknow U.P.]]></addr-line>
<country>India</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Dr. A.P.J. Abdul Kalam Technical University Centre for Advanced Studies ]]></institution>
<addr-line><![CDATA[Lucknow U.P.]]></addr-line>
<country>India</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,University of Las Palmas de Gran Canaria (ULPGC) Institute for Technological Development and Innovation in Communications (IDeT-IC) ]]></institution>
<addr-line><![CDATA[Las Palmas de G.C. ]]></addr-line>
<country>Spain</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>36</volume>
<numero>1</numero>
<fpage>449</fpage>
<lpage>457</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-34702022000100449&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-34702022000100449&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-34702022000100449&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Blood cell analysis is an important part of the health and immunity assessment. There are three major components of the blood: red blood cells, white blood cells, and platelets. The count and density of these blood cells are used to find multiple disorders like blood infections (anemia, leukemia, among others). Traditional methods are time-consuming, and the test cost is high. Thus, it arises the need for automated methods that can detect different kinds of blood cells and count the number of cells. A convolutional neural network-based framework is proposed for detecting and counting the cells. The neural network is trained for the multiple iterations, and a model having lower validation loss is saved. The experiments are done to analyze the performance of the detection system and results with high accuracy in the counting of the cells. The mean average precision is achieved when compared to ground truth provided to respective labels. The value of the average precision is found to be ranging from 70% to 99.1%, with a mean average precision value of 85.35%. The proposed framework had much less time complexity: it took only 0.111 seconds to process an image frame with dimensions of 640×480 pixels. The system can also be implemented in lowcost, single-board computers for rapid prototyping. The efficiency of the proposed framework to identify and count different blood cells can be utilized to assist medical professionals in finding disorders and making decisions based on the obtained report.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El análisis de células sanguíneas es una parte importante de la evaluación de la salud y la inmunidad. Hay tres componentes principales de los glóbulos rojos, los glóbulos blancos y las plaquetas. El recuento y la densidad de estas células sanguíneas se utilizan para encontrar múltiples trastornos como infecciones de la sangre como anemia, leucemia, etc. Los métodos tradicionales consumen mucho tiempo y el costo de las pruebas es alto. Por tanto, surge la necesidad de métodos automatizados que puedan detectar diferentes tipos de células sanguíneas y contar el número de células. Se propone un marco basado en una red neuronal convolucional para la detección y el recuento de las células. La red neuronal se entrena para las múltiples iteraciones y se guarda un modelo que tiene una menor pérdida de validación. Los experimentos se realizan con el fin de analizar el rendimiento del sistema de detección y los resultados con alta precisión en el recuento de células. La precisión promedio se logra al analizar las respectivas etiquetas que hay en la imagen. Se ha determinado que el valor de la precisión promedio, oscila entre el 70% y el 99,1% con un valor medio de 85,35%. El coste computacional de la propuesta fue de 0.111 segundos, procesar una imagen con dimensiones de 640 × 480 píxeles. El sistema también se puede implementar en ordenadores con CPU de bajo costo, para la creación rápida de prototipos. La eficiencia de la propuesta, para identificar y contar diferentes células sanguíneas, se puede utilizar para ayudar a los profesionales médicos a encontrar los trastornos y la toma decisiones, a partir de la identificación automática.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo O exame de células sanguíneas é uma parte importante da avaliação de saúde e imunidade. Há três componentes principais dos glóbulos vermelhos, glóbulos brancos e plaquetas. A contagem e a densidade dessas células sanguíneas são usadas para encontrar múltiplos distúrbios, tais como infecções no sangue: anemia, leucemia, etc. Os métodos tradicionais são demorados e o custo dos testes é alto. Portanto, surge a necessidade de métodos automatizados que possam detectar diferentes tipos de células sanguíneas e contar o número de células. É proposta uma estrutura baseada em rede neural convolucional para a detecção e contagem de células. A rede neural é treinada para múltiplas iterações e é salvo um modelo que tem uma menor perda de validação. São realizados experimentos para analisar o desempenho do sistema de detecção e os resultados com alta precisão na contagem de células. A precisão média é obtida analisando os respectivos rótulos na imagem. Foi determinado que o valor médio de precisão oscila entre 70 % e 99,1 % com um valor médio de 85,35 %. O custo computacional da proposta foi de 0,111 segundos, processando uma imagem com dimensões de 640 × 480 pixels. O sistema também pode ser implementado em computadores com CPUs de baixo custo para prototipagem rápida. A eficiência da proposta, para identificar e contar diferentes células sanguíneas, pode ser usada para ajudar os profissionais médicos a encontrar distúrbios e tomar decisões, com base na identificação automática.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[convolutional neural network]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[platelets, red blood cells]]></kwd>
<kwd lng="en"><![CDATA[white blood cells]]></kwd>
<kwd lng="es"><![CDATA[red neuronal convolucional]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[glóbulos rojos]]></kwd>
<kwd lng="es"><![CDATA[glóbulos blancos]]></kwd>
<kwd lng="pt"><![CDATA[Rede neural convolucional]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem profunda]]></kwd>
<kwd lng="pt"><![CDATA[glóbulos vermelhos]]></kwd>
<kwd lng="pt"><![CDATA[glóbulos brancos]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Acharjee]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Chakrabartty]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Alam]]></surname>
<given-names><![CDATA[M. I.]]></given-names>
</name>
<name>
<surname><![CDATA[Dey]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Santhi]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Ashour]]></surname>
<given-names><![CDATA[A. S.]]></given-names>
</name>
</person-group>
<source><![CDATA[A semiautomated approach using GUI for the detection of red blood cells. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT),]]></source>
<year>2016</year>
<page-range>525-9</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Acharya]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Kumar]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Identification and red blood cell automated counting from blood smear images using computer-aided system.]]></article-title>
<source><![CDATA[Medical &amp; Biological Engineering &amp; Computing]]></source>
<year>2018</year>
<volume>56</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>483-9</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alam]]></surname>
<given-names><![CDATA[M. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Islam]]></surname>
<given-names><![CDATA[M. T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning approach of automatic identification and counting of blood cells.]]></article-title>
<source><![CDATA[Healthcare Technology Letters]]></source>
<year>2019</year>
<volume>6</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>103-8</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alom]]></surname>
<given-names><![CDATA[M. Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Taha]]></surname>
<given-names><![CDATA[T. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Yakopcic]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Westberg]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Sidike]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Nasrin]]></surname>
<given-names><![CDATA[M. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Van Esesn]]></surname>
<given-names><![CDATA[B. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Awwal]]></surname>
<given-names><![CDATA[A. A. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Asari]]></surname>
<given-names><![CDATA[V. K.]]></given-names>
</name>
</person-group>
<source><![CDATA[The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.]]></source>
<year>2018</year>
</nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alomari]]></surname>
<given-names><![CDATA[Y. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Sheikh Abdullah]]></surname>
<given-names><![CDATA[S. N. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Zaharatul Azma]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Omar]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm.]]></article-title>
<source><![CDATA[Computational and Mathematical Methods in Medicine]]></source>
<year>2014</year>
<volume>2014</volume>
<page-range>1-17</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="book">
<collab>BCCD</collab>
<source><![CDATA[Blood Cell Count Dataset.]]></source>
<year>2020</year>
<publisher-name><![CDATA[GitHub, Inc]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cruz]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Jennifer]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Valiente-Castor]]></surname>
<given-names><![CDATA[L. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Mendoza]]></surname>
<given-names><![CDATA[C. M. T.]]></given-names>
</name>
<name>
<surname><![CDATA[Jay]]></surname>
<given-names><![CDATA[B. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Jane]]></surname>
<given-names><![CDATA[L. S. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Brian]]></surname>
<given-names><![CDATA[P. T. B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Determination of blood components (WBCs, RBCs, and Platelets) count in microscopic images using image processing and analysis.]]></article-title>
<source><![CDATA[2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)]]></source>
<year>2017</year>
<page-range>1-7</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Habibzadeh Motlagh]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Jannesari]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Rezaei]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Totonchi]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Baharvand]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception.]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Radeva]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Nikolaev]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Verikas]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Tenth International Conference on Machine Vision (ICMV 2017)]]></source>
<year>2018</year>
<page-range>105</page-range><publisher-name><![CDATA[SPIE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Krizhevsky]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Sutskever]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Hinton]]></surname>
<given-names><![CDATA[G. E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[ImageNet classification with deep convolutional neural networks.]]></article-title>
<source><![CDATA[Communications of the ACM]]></source>
<year>2017</year>
<volume>60</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>84-90</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[LeCun]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Bengio]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Hinton]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep learning]]></article-title>
<source><![CDATA[Nature]]></source>
<year>2015</year>
<volume>521</volume>
<numero>7553</numero>
<issue>7553</issue>
<page-range>436-44</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[T.-Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Dollar]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Girshick]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Hariharan]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Belongie]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Feature Pyramid Networks for Object Detection.]]></article-title>
<source><![CDATA[2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)]]></source>
<year>2017</year>
<page-range>936-44</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[T.-Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Maire]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Belongie]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Hays]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Perona]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Ramanan]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Dollár]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Zitnick]]></surname>
<given-names><![CDATA[C. L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Microsoft COCO: Common Objects in Context]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Fleet]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Pajdla]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Schiele]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Tuytelaars]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
</person-group>
<source><![CDATA[Computer Vision - ECCV 2014. ECCV 2014. Lecture Notes in Computer Science]]></source>
<year>2014</year>
<volume>8693</volume>
<page-range>740-55</page-range><publisher-name><![CDATA[Springer]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lou]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Yuan]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An automatic red blood cell counting method based on spectral images.]]></article-title>
<source><![CDATA[9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)]]></source>
<year>2016</year>
<volume>2016</volume>
<page-range>1391-6</page-range></nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Rong]]></given-names>
</name>
<name>
<surname><![CDATA[Liang]]></surname>
<given-names><![CDATA[Yuanzhi]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A self-adapting method for RBC count from different blood smears based on PCNN and image quality.]]></article-title>
<source><![CDATA[2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)]]></source>
<year>2016</year>
<page-range>1611-5</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sarrafzadeh]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Dehnavi]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Rabbani]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Ghane]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Talebi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Circlet based framework for red blood cells segmentation and counting.]]></article-title>
<source><![CDATA[IEEE Workshop on Signal Processing Systems (SiPS)]]></source>
<year>2015</year>
<volume>2015</volume>
<page-range>1-6</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
