Abstract
The primary objective of the research work presented is to design a thermal dose controller for hyperthermia treatment using several soft computing techniques, perform simulation studies on the mathematical model of the carcinogenic tissue and compare the controller performance. The non-linear relationship between temperature and dosage, after effects of treatment and constraints on normal tissue protection makes design of thermal dose controller a challenging task. Intelligent control of thermal dose using soft computing techniques like Fuzzy Logic Controller (FLC) and Fuzzy Adaptive Learning Control Network (FALCON) has been simulated on the tissue model and the performance of the controllers are compared in terms of accuracy of output and patient comfort. In the proposed system, optimum thermal dose to which the carcinogenic tissue can be exposed is determined. State space representation of the thermal model of the tissue is used for simulation studies. The performance metrics are the transient response characteristics like rise time, settling time and mean square error. These characteristics are compared to assess the controller performance. The FLC and FALCON performance for both linear and exponential parametrization has been compared. It has been observed that implementation of Fuzzy Controller and FALCON for thermal dose is successful in minimizing the treatment time. Model validation has been done by observing the mean square error which is well within the permissible limits. The concept of Automatic thermal dose controller has been enhanced using hybrid Neuro-fuzzy Controller (FALCON). The developed controller is applicable for a wide range of thermal therapies like thermal ablation and thermal radiotherapy.
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