Abstract
Combining metaheuristic algorithms with neural networks significantly enhances biomedical image segmentation and classification. Researchers optimize neural networks using evolutionary-based strategies like genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) to improve accuracy and reduce noise. These hybrid approaches help with feature selection, adaptive learning, and algorithm tuning, though computational limitations and parameter optimization remain challenges. Deep learning applications in biomedical imaging benefit from these optimizations, achieving better diagnostic precision and software-assisted clinical evaluations. Scalability and interpretability are essential for real-world deployment. Quantum-inspired metaheuristics also show promise in improving deep reinforcement learning, making image processing more efficient and robust. By integrating these techniques, both scientists and healthcare practitioners can advance their AI-based understanding of biomedical imaging.