Hybrid Metaheuristic Optimization for Neural Networks in Biomedical Imaging

Advancing biomedical image processing requires sophisticated optimization methods to improve segmentation and classification accuracy. This chapter explores hybrid metaheuristic approaches for enhancing neural networks in medical imaging, integrating evolutionary-inspired algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). These techniques optimize hyperparameters, enhance computational performance, and mitigate inconsistencies in deep learning models. By combining metaheuristic methods with deep learning, researchers address challenges such as medical image variability and operational noise, leading to more robust and efficient models. Case studies results confirm that hybrid optimization methods significantly improve segmentation and classification in biomedical imaging. Additionally, the chapter reviews existing applications and their role in future AI-driven healthcare solutions. It presents algorithm strategies, implementation techniques, and evaluation methods to aid experts in biomedical image analysis.

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