Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 796371, 9 pages
http://dx.doi.org/10.1155/2014/796371
Research Article

A Neural-Network-Based Approach to White Blood Cell Classification

1Department of Computer Science & Information Engineering, National Central University, Jhongli 32001, Taiwan
2General Hospital, Taipei 10656, Taiwan

Received 7 August 2013; Accepted 20 October 2013; Published 30 January 2014

Academic Editors: C.-C. Liu and C. H. Yeang

Copyright © 2014 Mu-Chun Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • Qanita Bani Baker, and Khaled Balhaf, “Exploiting GPUs to accelerate white blood cells segmentation in microscopic blood images,” 2017 8th International Conference on Information and Communication Systems (ICICS), pp. 136–140, . View at Publisher · View at Google Scholar
  • Xiang Li, Wei Li, Xiaodong Xu, and Wei Hu, “Cell classification using convolutional neural networks in medical hyperspectral imagery,” 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 501–504, . View at Publisher · View at Google Scholar
  • R.G Bagasjvara, Ika Candradewi, Sri Hartati, and Agus Harjoko, “Automated detection and classification techniques of Acute leukemia using image processing: A review,” 2016 2nd International Conference on Science and Technology-Computer (ICST), pp. 35–43, . View at Publisher · View at Google Scholar
  • Chuan Wang, Ying-Ge Chen, Jian-Li Gao, Gui-Yuan Lyu, Jie Su, Qi Zhang, Xin Ji, Ji-Zhong Yan, Qiao-Li Qiu, Yue-Li Zhang, Lin-Zi Li, Han-Ting Xu, and Su-Hong Chen, “Low local blood perfusion, high white blood cell and high platelet count are associated with primary tumor growth and lung metastasis in a 4T1 mouse b,” Oncology Letters, vol. 10, no. 2, pp. 754–760, 2015. View at Publisher · View at Google Scholar
  • Jaroonrut Prinyakupt, and Charnchai Pluempitiwiriyawej, “Segmentation of white blood cells and comparison of cell morphology by linear and naive Bayes classifiers,” Biomedical Engineering Online, vol. 14, 2015. View at Publisher · View at Google Scholar
  • Jianwei Zhao, Minshu Zhang, Zhenghua Zhou, Jianjun Chu, and Feilong Cao, “Automatic detection and classification of leukocytes using convolutional neural networks,” Medical & Biological Engineering & Computing, 2016. View at Publisher · View at Google Scholar
  • Seldyukov, Polyakov, and Nikitaev, “Research methodology of the artifact effect in the blood to the result of cell classification,” Journal of Physics: Conference Series, vol. 798, no. 1, 2017. View at Publisher · View at Google Scholar
  • Wei Li, Qiong Ran, Lan Chang, and Xiaofeng Xu, “Spatial-spectral blood cell classification with microscopic hyperspectral imagery,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 10461, 2017. View at Publisher · View at Google Scholar
  • Prayag Tiwari, Jia Qian, Qiuchi Li, Benyou Wang, Deepak Gupta, Ashish Khanna, and Joel J.P.C. Rodrigues, “Detection of Subtype Blood Cells using Deep Learning,” Cognitive Systems Research, 2018. View at Publisher · View at Google Scholar