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Overcoming Barriers in Metaheuristic Neural Network Optimization for Biomedical Imaging
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Author(s): S. Aarthi (Marwadi University, Rajkot, India), R. N. Ravikumar (Marwadi University, Rajkot, India), Madina Kalandarova (Mamun University, Khiva, Uzbekistan), Erkin Iskandarov (Urgench State University, Urgench, Uzbekistan), Nigina Khalikova (Termez University of Economics and Service, Termez, Uzbekistan)and Jayaraj Ramasamy (De Montfort University, Almaty, Kazakhstan)
Copyright: 2026
Pages: 34
Source title:
Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Source Author(s)/Editor(s): Prasanalakshmi Balaji (King Khalid University, Saudi Arabia), K. Martin Sagayam (Karunya Institute of Technology and Sciences, India), Aditi Sharma (Symbiosis International University, India)and Korhen Cengiz (University of Fujairah, UAE)
DOI: 10.4018/979-8-3373-0523-3.ch014
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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.
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