Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11546
Title: Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection
Authors: Wang, S
Chung, KFL 
Fu, D
Keywords: Boundary integrity
Fuzzy cellular neural networks
Parameter templates
White blood cell detection
Issue Date: 2007
Publisher: Elsevier
Source: Neurocomputing, 2007, v. 70, no. 7-9, p. 1348-1359 How to cite?
Journal: Neurocomputing 
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.
URI: http://hdl.handle.net/10397/11546
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2006.07.012
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