Investigation of Modified Bee Colony Algorithm for Brain Tumor Detection

Accurate segmentation of brain tumors from medical images is a crucial task for effective diagnosis and treatment planning. In this article, we propose a novel approach, the Modified Artificial Bee Colony Algorithm-Based Strategy (MABCAS), for brain tumor segmentation in magnetic resonance imaging (MRI) scans. The MABCAS algorithm is an enhanced version of the traditional Artificial Bee Colony (ABC) algorithm, inspired by the foraging behavior of honeybees. Through exploration and exploitation phases, MABCAS efficiently searches the solution space to identify optimal segmentation solutions for complex brain tumor shapes. We begin by discussing the challenges faced in brain tumor diagnosis and limitations of existing segmentation methods. We then present the details of MABCAS implementation and customization for brain tumor segmentation, emphasizing modifications to improve convergence speed and handle irregular tumor boundaries effectively. In conclusion, the Modified Artificial Bee Colony Algorithm-Based Strategy offers a transformative approach to brain tumor segmentation, providing a strong foundation for advancing medical image analysis and improving patient care. This research contributes to the growing field of AI in healthcare, encouraging further investigations and collaborations in this promising domain.

کلیدواژه ها:

Brain Tumor Segmentation ، Modified Artificial Bee Colony Algorithm Medical Image Analysis ، Algorithm Optimization ، MRI Scans