الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
Cancer is one of the leading causes of morbidity and mortality worldwide. It is a highly
complex group of diseases, involving uncontrolled abnormal cells growth; namely tumors.
These cancer tumors can potentially infect the normal tissues of vital organs, e.g. the liver,
kidney, and lung. One among the foremost common treatments of cancer
is chemotherapy that aims to kill abnormal cells; but also, organs of the patients and normal
cells are affected. In practice, it is typically tough to keep up optimum chemotherapy
doses that may maximize the abnormal cell killing additionally as reducing side effects.
To make the chemotherapeutical treatment effective, optimum drug planning is needed
to balance between the helpful and poisonous effects of the cancer drugs. Standard clinical
strategies often fail to search out the successful drug doses because of their inherent
conflicting nature.
The main objective of this thesis is to design a closed loop control system that is able to
achieve the effective treatment with the least dose of chemotherapy by merging expert
knowledge into automated closed-loop control of chemotherapy drug infusion. This control
system must achieve safety elements for cancer patient such as toxicity limitation in the
body and maximum limit for the chemotherapy dose.
This thesis presents different control schemes for regulating intravenous chemotherapy
drug delivery, based on intelligent controller with optimization algorithms. Using the
mathematical model for cancer chemotherapy drug scheduling, the developed controllers
constitutes the following two contributions: First, leveraging objectives of cancer tumor
treatment are accomplished by combining the advantages of advanced closed-loop
chemotherapy infusion control and optimization in a new framework. Second, clinical
constraints; namely maximum allowed levels of both drug infusion dose and drug toxicity
are implied in the design of optimized adaptive controller to ensure the medical safety of
cancer patients.
Fuzzy logic controller (FLC) is employed to merge expert physician knowledge into
automated closed-loop control scheme of intravenous chemotherapy drug infusion.
Furthermore, this drug infusion controller has been implemented using a microcontroller
and tested in the hardware-in-the loop simulation framework to verify its robust
performance.
Optimal intuitionistic fuzzy logic control (IFLC) of chemotherapy drug delivery system
is then presented in this thesis using two different optimization methods (Invasive Weed
Optimization (IWO) and Ant Colony Optimization (ACO)). These optimization algorithms
are utilized to optimize the intuitionistic fuzzy input-output scaling factors of the drug
infusion system. The controller is designed and tested for tracking two clinically relevant
chemotherapy dose scheduling protocols. Moreover, the patient safety is implied in the
design of controller by considering maximum allowed level constraints on both drug
infusion dose and drug-dose toxicity.
Furthermore, computational results and comparative evaluations showed that the
developed intuitionistic fuzzy controller outperforms other controllers in previous related
studies to effectively minimize the tumor size at the end of cancer treatment time. Finally,
this study showed that the patient sensitivity to toxicity has a great impact on the
performance of cancer treatment and a suitable reference input scheduled within each cycle
of treatment period has to be considered carefully.