Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Digital Image Processing
A Statistical 3-D Segmentation Algorithm for Classifying Brain Tissues in Multiple Sclerosis
CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
Integrating fuzzy knowledge by genetic algorithms
IEEE Transactions on Evolutionary Computation
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
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This paper proposes a soft-computing based diagnostic tool for analyzing (white matter changes) demyelination due to radiation therapy given to brain tumor cases. The tool exploits the pattern of changes in gray level distribution using a temporal sequence of magnetic resonance (MR) images. Appearance of white matter changes due to demyelination varies from patient to patient. Further, there exists inherent impreciseness in the white matter change patterns. These characteristics make use of fuzzy features well suited for describing image based temporal patterns. Correlation between these temporal patterns and actual onset of demyelination can be captured by fuzzy rules because of the inherent uncertainty associated with changes in gray level pattern in the image and occurrence of the disease. The tool is based on hybrid approach of two popular approaches of genetic algorithm based machine learning (GBML) techniques namely Michigan and Pittsburgh approach. The genetic algorithm (GA) based machine learning tool generates an optimized rule set to indicate positive (P), negative (N) or doubtful (D) cases of demyelination.