Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
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Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
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CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Topographic Maps of Color Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Cytological image analysis with a genetic fuzzy finite state machine
Computer Methods and Programs in Biomedicine
A design methodology for fuzzy system interfaces
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Artificial Intelligence in Medicine
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Journal of Artificial Evolution and Applications
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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Objective: The objective of this research is to carry out the classification of cellular nuclei in cytological pleural fluid images. The article focuses on the feature extraction and classification processes. The extracted feature is a spatial measurement of the chromatin distribution in cellular nuclei. The designed classifiers are fuzzy classifiers that carry out supervised classification. The classifier system's inputs are data series that represent these texture measurements. Methods and material: The classifier is built on a Recurrent Fuzzy System (RFS). An evolutionary algorithm inspired by the Michigan approach is used to find an optimal RFS to classify different patterns expressed as data series. Results: The effectiveness of the proposed classifier system is compared with other existing classification methods and evaluated via Receiver Operating Characteristic (ROC) analysis. We have obtained RFS based classifiers that perform with sensitivity values between 82.26 and 93.55% and with specificity values between 80.65 and 90.32%. The behavior of the proposed chromatin measurement is also compared with other texture measurements. Conclusion: The RFS based classifiers were successfully applied to the proposed data series that represent the chromatin distribution in cellular nuclei. These fuzzy classifiers present the highest classification efficiency and the ROC analysis confirms their suitable behavior.