A fuzzy system for evaluating radiation treatment plans of head and neck cancer
- 1 May 2012
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
In this study, 14 treatment planning dose parameters for head and neck cancer are adopted as the characteristic functions to establish the statistical analysis of the fuzzy system module for radiation treatment plan classification. By constructing fuzzy rules and fuzzy set membership functions, we aim to improve the classification efficiency and accuracy by the fuzzy systems, and to improve the quality of the treatment plan. Three different fuzzy logic control systems were used for analysis of 100 Pinnacle treatment planning system datasets. The results show that the accuracy may be up to 100%. The fuzzy logic control system we propose may be a useful tool for accurate planning and decision-making.Keywords
This publication has 11 references indexed in Scilit:
- Design of Control System of Hydrogen and Oxygen Flow Rate for Proton Exchange Membrane Fuel Cell Using Fuzzy Logic ControllerEnergy Procedia, 2011
- Dosimetric Comparison of Helical Tomotherapy and Dynamic Conformal Arc Therapy in Stereotactic Radiosurgery for Vestibular SchwannomasMedical Dosimetry, 2011
- A Multilevel Inverter for Photovoltaic Systems With Fuzzy Logic ControlIEEE Transactions on Industrial Electronics, 2010
- Nonlinear system identification via Laguerre network based fuzzy systemsFuzzy Sets and Systems, 2009
- Dosimetric comparisons of helical tomotherapy and step-and-shoot intensity-modulated radiotherapy in nasopharyngeal carcinomaRadiotherapy and Oncology, 2008
- Fuzzy-UCS: A Michigan-Style Learning Fuzzy-Classifier System for Supervised LearningIEEE Transactions on Evolutionary Computation, 2008
- Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithmsInternational Journal of Approximate Reasoning, 2008
- Fuzzy Interpolative Reasoning for Sparse Fuzzy-Rule-Based Systems Based on the Areas of Fuzzy SetsIEEE Transactions on Fuzzy Systems, 2008
- Fuzzy interpolative reasoning via scale and move transformationsIEEE Transactions on Fuzzy Systems, 2006
- Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostateRadiotherapy and Oncology, 2004