Supervised neural networks for the automatic classification of leukocytes in blood microscope images

Sheng-Fuu Lin, Chien Hao Tseng, Chung I. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, the application of the supervised learning system to automatic classification of leukocytes processing for the microscopic images analysis is presented. The traditional pattern classification in cellular images is typically made by experienced operators. Such procedures may present a non-standard and unstable accuracy when it depends on the operator's capabilities and tiredness. In this study, we propose the supervised learning system to achieve an automated segmentation and classification of leukocytes based on supervised neural networks and image processing methods. The experimental results show that the proposed automatic classification learning system can effectively classify the five types of the leukocytes in microscopic cell images, as well as to compare the classification results to those obtained by the medical experts.

Original languageEnglish
Title of host publicationApplied Science and Precision Engineering Innovation
Pages491-495
Number of pages5
DOIs
StatePublished - 1 Jan 2014
EventInternational Applied Science and Precision Engineering Conference 2013, ASPEC 2013 - NanTou, Taiwan
Duration: 18 Oct 201322 Oct 2013

Publication series

NameApplied Mechanics and Materials
Volume479-480
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

ConferenceInternational Applied Science and Precision Engineering Conference 2013, ASPEC 2013
CountryTaiwan
CityNanTou
Period18/10/1322/10/13

Keywords

  • Automatic cell classification
  • Image processing
  • Morphological analysis
  • Multilayer perceptron (MLP)

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    Lin, S-F., Tseng, C. H., & Huang, C. I. (2014). Supervised neural networks for the automatic classification of leukocytes in blood microscope images. In Applied Science and Precision Engineering Innovation (pp. 491-495). (Applied Mechanics and Materials; Vol. 479-480). https://doi.org/10.4028/www.scientific.net/AMM.479-480.491