Neural Network Classification of White Blood Cell using Microscopic Images
With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed...
Gespeichert in:
| Veröffentlicht in: | International journal of advanced computer science & applications 2017-01, Vol.8 (5) |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 5 |
| container_start_page | |
| container_title | International journal of advanced computer science & applications |
| container_volume | 8 |
| creator | Othman, Mazin Z Mohammed, Thabit S Ali, Alaa B |
| description | With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed forward back propagation neural network. After segmentation of blood cells that are obtained from microscopic images, the most 16 significant features of these cells are fed as inputs to the neural network. Half of the 100 of the WBC sub-images that are found after segmentation are used to train the neural network, while the other half is used for test. The results found are promising with classification accuracy being 96%. |
| doi_str_mv | 10.14569/IJACSA.2017.080513 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2656447747</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2656447747</sourcerecordid><originalsourceid>FETCH-LOGICAL-c252t-917130feb8a673a999c0d88056b4185b123284ea035d1411d73fca5eac5f35f23</originalsourceid><addsrcrecordid>eNotkMtOwzAURC0EEhX0C9hYYp3it51liXi0KmUBCHaW69jFJa2LnQjx96QNdzN3MZoZHQCuMJpgxkV5M5tPq5fphCAsJ0ghjukJGBHMRcG5RKfHXxUYyY9zMM55g_qjJRGKjsB86bpkGrh07U9MX7BqTM7BB2vaEHcwevj-GVoHb5sYa1i5poFdDrs1fAo2xWzjPlg425q1y5fgzJsmu_G_XoC3-7vX6rFYPD_MqumisISTtiixxBR5t1JGSGrKsrSoVv1ssWJY8RUmlCjmDKK8xgzjWlJvDXfGck-5J_QCXA-5-xS_O5dbvYld2vWVmgguGJOSyd5FB9dhZk7O630KW5N-NUb6yE0P3PSBmx640T9dGV9U</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2656447747</pqid></control><display><type>article</type><title>Neural Network Classification of White Blood Cell using Microscopic Images</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Othman, Mazin Z ; Mohammed, Thabit S ; Ali, Alaa B</creator><creatorcontrib>Othman, Mazin Z ; Mohammed, Thabit S ; Ali, Alaa B</creatorcontrib><description>With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed forward back propagation neural network. After segmentation of blood cells that are obtained from microscopic images, the most 16 significant features of these cells are fed as inputs to the neural network. Half of the 100 of the WBC sub-images that are found after segmentation are used to train the neural network, while the other half is used for test. The results found are promising with classification accuracy being 96%.</description><identifier>ISSN: 2158-107X</identifier><identifier>EISSN: 2156-5570</identifier><identifier>DOI: 10.14569/IJACSA.2017.080513</identifier><language>eng</language><publisher>West Yorkshire: Science and Information (SAI) Organization Limited</publisher><subject>Back propagation networks ; Blood ; Image classification ; Image segmentation ; Leukocytes ; Medical imaging ; Neural networks ; Object recognition</subject><ispartof>International journal of advanced computer science & applications, 2017-01, Vol.8 (5)</ispartof><rights>2017. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c252t-917130feb8a673a999c0d88056b4185b123284ea035d1411d73fca5eac5f35f23</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Othman, Mazin Z</creatorcontrib><creatorcontrib>Mohammed, Thabit S</creatorcontrib><creatorcontrib>Ali, Alaa B</creatorcontrib><title>Neural Network Classification of White Blood Cell using Microscopic Images</title><title>International journal of advanced computer science & applications</title><description>With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed forward back propagation neural network. After segmentation of blood cells that are obtained from microscopic images, the most 16 significant features of these cells are fed as inputs to the neural network. Half of the 100 of the WBC sub-images that are found after segmentation are used to train the neural network, while the other half is used for test. The results found are promising with classification accuracy being 96%.</description><subject>Back propagation networks</subject><subject>Blood</subject><subject>Image classification</subject><subject>Image segmentation</subject><subject>Leukocytes</subject><subject>Medical imaging</subject><subject>Neural networks</subject><subject>Object recognition</subject><issn>2158-107X</issn><issn>2156-5570</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNotkMtOwzAURC0EEhX0C9hYYp3it51liXi0KmUBCHaW69jFJa2LnQjx96QNdzN3MZoZHQCuMJpgxkV5M5tPq5fphCAsJ0ghjukJGBHMRcG5RKfHXxUYyY9zMM55g_qjJRGKjsB86bpkGrh07U9MX7BqTM7BB2vaEHcwevj-GVoHb5sYa1i5poFdDrs1fAo2xWzjPlg425q1y5fgzJsmu_G_XoC3-7vX6rFYPD_MqumisISTtiixxBR5t1JGSGrKsrSoVv1ssWJY8RUmlCjmDKK8xgzjWlJvDXfGck-5J_QCXA-5-xS_O5dbvYld2vWVmgguGJOSyd5FB9dhZk7O630KW5N-NUb6yE0P3PSBmx640T9dGV9U</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Othman, Mazin Z</creator><creator>Mohammed, Thabit S</creator><creator>Ali, Alaa B</creator><general>Science and Information (SAI) Organization Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20170101</creationdate><title>Neural Network Classification of White Blood Cell using Microscopic Images</title><author>Othman, Mazin Z ; Mohammed, Thabit S ; Ali, Alaa B</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c252t-917130feb8a673a999c0d88056b4185b123284ea035d1411d73fca5eac5f35f23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Back propagation networks</topic><topic>Blood</topic><topic>Image classification</topic><topic>Image segmentation</topic><topic>Leukocytes</topic><topic>Medical imaging</topic><topic>Neural networks</topic><topic>Object recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Othman, Mazin Z</creatorcontrib><creatorcontrib>Mohammed, Thabit S</creatorcontrib><creatorcontrib>Ali, Alaa B</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of advanced computer science & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Othman, Mazin Z</au><au>Mohammed, Thabit S</au><au>Ali, Alaa B</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural Network Classification of White Blood Cell using Microscopic Images</atitle><jtitle>International journal of advanced computer science & applications</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>8</volume><issue>5</issue><issn>2158-107X</issn><eissn>2156-5570</eissn><abstract>With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed forward back propagation neural network. After segmentation of blood cells that are obtained from microscopic images, the most 16 significant features of these cells are fed as inputs to the neural network. Half of the 100 of the WBC sub-images that are found after segmentation are used to train the neural network, while the other half is used for test. The results found are promising with classification accuracy being 96%.</abstract><cop>West Yorkshire</cop><pub>Science and Information (SAI) Organization Limited</pub><doi>10.14569/IJACSA.2017.080513</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2158-107X |
| ispartof | International journal of advanced computer science & applications, 2017-01, Vol.8 (5) |
| issn | 2158-107X 2156-5570 |
| language | eng |
| recordid | cdi_proquest_journals_2656447747 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Back propagation networks Blood Image classification Image segmentation Leukocytes Medical imaging Neural networks Object recognition |
| title | Neural Network Classification of White Blood Cell using Microscopic Images |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T09%3A13%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Neural%20Network%20Classification%20of%20White%20Blood%20Cell%20using%20Microscopic%20Images&rft.jtitle=International%20journal%20of%20advanced%20computer%20science%20&%20applications&rft.au=Othman,%20Mazin%20Z&rft.date=2017-01-01&rft.volume=8&rft.issue=5&rft.issn=2158-107X&rft.eissn=2156-5570&rft_id=info:doi/10.14569/IJACSA.2017.080513&rft_dat=%3Cproquest_cross%3E2656447747%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2656447747&rft_id=info:pmid/&rfr_iscdi=true |