Abstract
Abstract: Brain tumor detection is a critical task in medical imaging that requires accurate and efficient image segmentation techniques. This paper presents a comparative study of two advanced segmentation methods: Fuzzy Level Set Search and Rescue Optimization (FLSSRO) and Clustering-Based Segmentation (CBS). By examining the theoretical foundations, algorithmic structures, and performance metrics of these techniques, we aim to highlight their strengths and limitations in the context of brain tumor detection. Through experimental evaluation on benchmark MRI datasets, we analyze the efficacy, accuracy, and computational efficiency of both methods. The study concludes with insights into the appropriate application domains for each technique and potential future research directions.
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