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Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
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Author(s): R. RoselinKiruba (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India), M. Vasumathy (Kingston Engineering College, India), J. P. Shritharanyaa (VIT Bhopal University, India), C. Saranya Jothi (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India), L. Sharmila (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India)and J. Jude Moses Anto Devakanth (Madanapalle Institute of Technology & Science, India)
Copyright: 2026
Pages: 28
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.ch015
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Abstract
Biomedical image segmentation plays a critical role in medical diagnostics, enabling precise identification of pathological regions in various medical imaging modalities. However, existing deep learning-based segmentation models often suffer from high computational complexity, increased inference time, and suboptimal accuracy due to inefficient feature extraction. To address these challenges, we propose MO-FANet, a metaheuristic-optimized deep learning framework that integrates Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for enhanced feature selection and segmentation accuracy. The proposed model significantly reduces computational cost while improving segmentation performance. Furthermore, our model reduces parameters to 6.89M, making it lightweight and efficient for real-time medical applications. The results confirm the effectiveness of MO-FANet in achieving superior segmentation performance while ensuring computational efficiency.
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