Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised segmentation of ultrasonic liver images by multiresolution fractal feature vector
Information Sciences: an International Journal
System-level training of neural networks for counting white bloodcells
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Information Technology in Biomedicine
On new fuzzy morphological associative memories
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Leakage Delays in T---S Fuzzy Cellular Neural Networks
Neural Processing Letters
Expert Systems with Applications: An International Journal
Denial of service detection with hybrid fuzzy set based feed forward neural network
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.01 |
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.