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Brain Cancer Cell Detection Optimization Schemes Using Image Processing and Soft Computing

  • Chudapa Thammasakorn
  • Chakchai So-In
  • Wiyada Punjaruk
  • Urachart Kokaew
  • Boonsup Waikham
  • Songyut Permpol
  • Phet Aimtongkham
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 362)

Abstract

This paper introduces a novel methodology to automatically measure a number of brain cancer cells using optimized image processing and soft-computing for classification. The former approach is used to prepare the cell image from the medical laboratory, such as background removal, image adjustment, and cell detection including noise reduction. Then, Gabor filter is applied to retrieve the key features before feeding into different soft-computing techniques to identify the actual cells. The results show that the performance of Fuzzy C-Mean with image processing optimization is outstanding compared to neural networks, genetic algorithms, and support vector machines, i.e., 96 % versus less than 90 % in precision, in addition to the superior computational time of around two seconds.

Keywords

Brain cancer cell Cell counting Cell detection Image processing Machine learning Soft computing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Chudapa Thammasakorn
    • 1
  • Chakchai So-In
    • 1
  • Wiyada Punjaruk
    • 2
  • Urachart Kokaew
    • 1
  • Boonsup Waikham
    • 1
  • Songyut Permpol
    • 1
  • Phet Aimtongkham
    • 1
  1. 1.Applied Network Technology (ANT), Department of Computer Science, Faculty of ScienceKhon Kaen UniversityKhon KaenThailand
  2. 2.Department of Physiology, Faculty of MedicineKhon Kaen UniversityKhon KaenThailand

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