Neutrosophic Fuzzy Hybrid Method Using Metaheuristic Algorithms for IMRT Treatment

Intensity-Modulated Radiation Therapy (IMRT) has gained prominence in cancer treatment due to its ability to deliver precise radiation doses to tumor regions while sparing adjacent healthy tissues. Optimizing the treatment planning process in IMRT involves addressing complex, conflicting objectives and uncertainties inherent in medical decision-making. This paper proposes a novel Neutrosophic Fuzzy Hybrid Method (NFHM) integrated with metaheuristic algorithms to enhance the efficiency and robustness of IMRT treatment planning. The Neutrosophic Fuzzy Hybrid Method combines neutrosophic logic, fuzzy logic, and metaheuristic optimization techniques to model and handle uncertainties, imprecisions, and conflicting information present in the IMRT treatment planning domain. Neutrosophic logic provides a valuable framework for representing indeterminacy, while fuzzy logic aids in capturing vagueness in medical data. To optimize IMRT treatment plans, the proposed method incorporates metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). These algorithms explore the solution space efficiently, seeking optimal trade-offs among conflicting treatment objectives, including target coverage, organ-at-risk sparing, and dose uniformity. The performance of the Neutrosophic Fuzzy Hybrid Method is evaluated using benchmark datasets and compared against traditional optimization approaches. The results demonstrate the method's superiority in generating high-quality IMRT plans that exhibit improved dose distribution and conformity while considering uncertainties and imprecisions. In conclusion, the integration of neutrosophic and fuzzy reasoning with metaheuristic optimization algorithms in the proposed NFHM presents a promising approach for enhancing the IMRT treatment planning process. The method's ability to handle uncertainties and find optimal solutions makes it a valuable tool for improving the efficacy and precision of cancer treatments through IMRT.