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Image segmentation is a crucial aspect of clinical decision-making in the medical field. The integration of image segmentation techniques has dramatically enhanced healthcare delivery. Also, the advancement of deep learning, particularly Convolutional Neural Networks (CNNs), has brought about a significant transformation in medical image analysis. These advanced algorithms have shown exceptional abilities in identifying complex patterns and features in medical images, revolutionizing diagnostic imaging. However, the complexity and scale of these models present significant challenges. This requires a substantial amount of computational resources and expert knowledge for successful implementation. Addressing these challenges is crucial to fully exploit the potential of deep learning in the field of medical image segmentation. To address the challenges, this study combines metaheuristic optimization algorithms with deep learning. These algorithms, inspired by natural processes, provide an effective way to optimize the structure and parameters of CNNs, thus making the process of medical image segmentation more efficient.