Image Analysis Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks and Artificial Intelligence for Biomedical Engineering
Neural Networks and Artificial Intelligence for Biomedical Engineering
Digital Image Processing
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
New dynamical optimal learning for linear multilayer FNN
IEEE Transactions on Neural Networks
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In this paper a novel fast fuzzy backpropagation algorithm for classification of colon cell images is proposed. The experimental results show that the accuracy of the method is very high. The algorithm is evaluated using 116 cancer suspects and 88 normal colon cells images and results in a classification rate of 96.4%. The method automatically detects differences in biopsy images of the colorectal polyps, extracts the required image texture features and then classifies the cells into normal and cancer respectively. The net function computation is significantly faster. Convergence is quicker. It has an added advantage of being independent of the feature extraction procedure adopted, with knowledge and learning to overcome the sharpness of class characteristics associated with other classifiers algorithms. It can also be used to resolve a situation of in-between classes.