Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection

Wang Shitong, Fu Lai Korris Chung, Fu Duan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

54 Citations (Scopus)

Abstract

Although algorithm NDA based on the fuzzy cellular neural network (FCNN) has indicated the basic superiority in its adaptability and easy hardware-realization for microscopic white blood cell detection [Wang Shitong, Wang Min, A new algorithm NDA based on fuzzy cellular neural networks for white blood cell detection, IEEE Trans. Inf. Technol. Biomed., accepted], it still does not work very well in keeping the boundary integrity of a white blood cell. In this paper, the improved version of FCNN called IFCNN is proposed to tackle this issue. The distinctive characteristic of IFCNN is to incorporate the novel fuzzy status containing the useful information beyond a white blood cell into its state equation, resulting in enhancing the boundary integrity. Our theoretical analysis shows that IFCNN has the global stability and the experimental results demonstrate its obvious advantage over FCNN in keeping the boundary integrity.
Original languageEnglish
Pages (from-to)1348-1359
Number of pages12
JournalNeurocomputing
Volume70
Issue number7-9
DOIs
Publication statusPublished - 1 Mar 2007

Keywords

  • Boundary integrity
  • Fuzzy cellular neural networks
  • Parameter templates
  • White blood cell detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Cellular and Molecular Neuroscience

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