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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Automated recognition of white blood cells using deep learning</title><author><name sortKey="Khouani, Amin" sort="Khouani, Amin" uniqKey="Khouani A" first="Amin" last="Khouani">Amin Khouani</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.12319.38</institution-id><institution-id institution-id-type="ISNI">0000 0004 0370 1320</institution-id><institution>University of Tlemcen,</institution></institution-wrap>Tlemcen, Algeria</nlm:aff></affiliation></author><author><name sortKey="El Habib Daho, Mostafa" sort="El Habib Daho, Mostafa" uniqKey="El Habib Daho M" first="Mostafa" last="El Habib Daho">Mostafa El Habib Daho</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.12319.38</institution-id><institution-id institution-id-type="ISNI">0000 0004 0370 1320</institution-id><institution>University of Tlemcen,</institution></institution-wrap>Tlemcen, Algeria</nlm:aff></affiliation></author><author><name sortKey="Mahmoudi, Sidi Ahmed" sort="Mahmoudi, Sidi Ahmed" uniqKey="Mahmoudi S" first="Sidi Ahmed" last="Mahmoudi">Sidi Ahmed Mahmoudi</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.8364.9</institution-id><institution-id institution-id-type="ISNI">0000 0001 2184 581X</institution-id><institution>University of Mons,</institution></institution-wrap>20 Parc Sq., 7000 Mons, Belgium</nlm:aff></affiliation></author><author><name sortKey="Chikh, Mohammed Amine" sort="Chikh, Mohammed Amine" uniqKey="Chikh M" first="Mohammed Amine" last="Chikh">Mohammed Amine Chikh</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.12319.38</institution-id><institution-id institution-id-type="ISNI">0000 0004 0370 1320</institution-id><institution>University of Tlemcen,</institution></institution-wrap>Tlemcen, Algeria</nlm:aff></affiliation></author><author><name sortKey="Benzineb, Brahim" sort="Benzineb, Brahim" uniqKey="Benzineb B" first="Brahim" last="Benzineb">Brahim Benzineb</name><affiliation><nlm:aff id="Aff3">CHU of Tlemcen, Tlemcen, Algeria</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32850177</idno><idno type="pmc">7438424</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438424</idno><idno type="RBID">PMC:7438424</idno><idno type="doi">10.1007/s13534-020-00168-3</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000021</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000021</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Automated recognition of white blood cells using deep learning</title><author><name sortKey="Khouani, Amin" sort="Khouani, Amin" uniqKey="Khouani A" first="Amin" last="Khouani">Amin Khouani</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.12319.38</institution-id><institution-id institution-id-type="ISNI">0000 0004 0370 1320</institution-id><institution>University of Tlemcen,</institution></institution-wrap>Tlemcen, Algeria</nlm:aff></affiliation></author><author><name sortKey="El Habib Daho, Mostafa" sort="El Habib Daho, Mostafa" uniqKey="El Habib Daho M" first="Mostafa" last="El Habib Daho">Mostafa El Habib Daho</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.12319.38</institution-id><institution-id institution-id-type="ISNI">0000 0004 0370 1320</institution-id><institution>University of Tlemcen,</institution></institution-wrap>Tlemcen, Algeria</nlm:aff></affiliation></author><author><name sortKey="Mahmoudi, Sidi Ahmed" sort="Mahmoudi, Sidi Ahmed" uniqKey="Mahmoudi S" first="Sidi Ahmed" last="Mahmoudi">Sidi Ahmed Mahmoudi</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="GRID">grid.8364.9</institution-id><institution-id institution-id-type="ISNI">0000 0001 2184 581X</institution-id><institution>University of Mons,</institution></institution-wrap>20 Parc Sq., 7000 Mons, Belgium</nlm:aff></affiliation></author><author><name sortKey="Chikh, Mohammed Amine" sort="Chikh, Mohammed Amine" uniqKey="Chikh M" first="Mohammed Amine" last="Chikh">Mohammed Amine Chikh</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.12319.38</institution-id><institution-id institution-id-type="ISNI">0000 0004 0370 1320</institution-id><institution>University of Tlemcen,</institution></institution-wrap>Tlemcen, Algeria</nlm:aff></affiliation></author><author><name sortKey="Benzineb, Brahim" sort="Benzineb, Brahim" uniqKey="Benzineb B" first="Brahim" last="Benzineb">Brahim Benzineb</name><affiliation><nlm:aff id="Aff3">CHU of Tlemcen, Tlemcen, Algeria</nlm:aff></affiliation></author></analytic><series><title level="j">Biomedical Engineering Letters</title><idno type="ISSN">2093-9868</idno><idno type="eISSN">2093-985X</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="Par1">The detection, counting, and precise segmentation of white blood cells in cytological images are vital steps in the effective diagnosis of several cancers. This paper introduces an efficient method for automatic recognition of white blood cells in peripheral blood and bone marrow images based on deep learning to alleviate tedious tasks for hematologists in clinical practice. First, input image pre-processing was proposed before applying a deep neural network model adapted to cells localization and segmentation. Then, model outputs were improved by using combined predictions and corrections. Finally, a new algorithm that uses the cooperation between model results and spatial information was implemented to improve the segmentation quality. To implement our model, python language, Tensorflow, and Keras libraries were used. The calculations were executed using NVIDIA GPU 1080, while the datasets used in our experiments came from patients in the Hemobiology service of Tlemcen Hospital (Algeria). The results were promising and showed the efficiency, power, and speed of the proposed method compared to the state-of-the-art methods. In addition to its accuracy of 95.73%, the proposed approach provided fast predictions (less than 1 s).</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Biomed Eng Lett</journal-id><journal-id journal-id-type="iso-abbrev">Biomed Eng Lett</journal-id><journal-title-group><journal-title>Biomedical Engineering Letters</journal-title></journal-title-group><issn pub-type="ppub">2093-9868</issn><issn pub-type="epub">2093-985X</issn><publisher><publisher-name>The Korean Society of Medical and Biological Engineering</publisher-name><publisher-loc>Korea</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32850177</article-id><article-id pub-id-type="pmc">7438424</article-id><article-id pub-id-type="publisher-id">168</article-id><article-id pub-id-type="doi">10.1007/s13534-020-00168-3</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Article</subject></subj-group></article-categories><title-group><article-title>Automated recognition of white blood cells using deep learning</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-4428-5146</contrib-id><name><surname>Khouani</surname><given-names>Amin</given-names></name><address><email>amin.khouani@univ-tlemcen.dz</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>El Habib Daho</surname><given-names>Mostafa</given-names></name><address><email>mostafa.elhabibdaho@univ-tlemcen.dz</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Mahmoudi</surname><given-names>Sidi Ahmed</given-names></name><address><email>Sidi.MAHMOUDI@umons.ac.be</email></address><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Chikh</surname><given-names>Mohammed Amine</given-names></name><address><email>mohammedamine.chikh@univ-tlemcen.dz</email></address><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Benzineb</surname><given-names>Brahim</given-names></name><address><email>benzineb.brahim@yahoo.fr</email></address><xref ref-type="aff" rid="Aff3">3</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="GRID">grid.12319.38</institution-id><institution-id institution-id-type="ISNI">0000 0004 0370 1320</institution-id><institution>University of Tlemcen,</institution></institution-wrap>Tlemcen, Algeria</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="GRID">grid.8364.9</institution-id><institution-id institution-id-type="ISNI">0000 0001 2184 581X</institution-id><institution>University of Mons,</institution></institution-wrap>20 Parc Sq., 7000 Mons, Belgium</aff><aff id="Aff3"><label>3</label>CHU of Tlemcen, Tlemcen, Algeria</aff></contrib-group><pub-date pub-type="epub"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type="collection"><month>8</month><year>2020</year></pub-date><volume>10</volume><issue>3</issue><fpage>359</fpage><lpage>367</lpage><history><date date-type="received"><day>4</day><month>3</month><year>2020</year></date><date date-type="rev-recd"><day>17</day><month>7</month><year>2020</year></date><date date-type="accepted"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© Korean Society of Medical and Biological Engineering 2020</copyright-statement></permissions><abstract id="Abs1"><p id="Par1">The detection, counting, and precise segmentation of white blood cells in cytological images are vital steps in the effective diagnosis of several cancers. This paper introduces an efficient method for automatic recognition of white blood cells in peripheral blood and bone marrow images based on deep learning to alleviate tedious tasks for hematologists in clinical practice. First, input image pre-processing was proposed before applying a deep neural network model adapted to cells localization and segmentation. Then, model outputs were improved by using combined predictions and corrections. Finally, a new algorithm that uses the cooperation between model results and spatial information was implemented to improve the segmentation quality. To implement our model, python language, Tensorflow, and Keras libraries were used. The calculations were executed using NVIDIA GPU 1080, while the datasets used in our experiments came from patients in the Hemobiology service of Tlemcen Hospital (Algeria). The results were promising and showed the efficiency, power, and speed of the proposed method compared to the state-of-the-art methods. In addition to its accuracy of 95.73%, the proposed approach provided fast predictions (less than 1 s).</p></abstract><kwd-group xml:lang="en"><title>Keywords</title><kwd>Deep learning</kwd><kwd>White blood cells</kwd><kwd>Image segmentation</kwd><kwd>Classification</kwd><kwd>Mask RCNN</kwd><kwd>Object detection</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© Korean Society of Medical and Biological Engineering 2020</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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