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Metaheuristic Optimization for Generative and Explainable AI in Biomedical Imaging
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Author(s): Ketan Sarvakar (Ganpat University, India), Chandrakant Devabhai Patel (Ganpat University, India), Hiral B. Patel (Ganpat University, India), Rakshaben Karshandas Patel (Ganpat University, India), Aniket Patel (Ganpat University, India), Paresh M. Solanki (Ganpat University, India), Meghna Babubhai Patel (A.M. Patel Institute of Computer Studies, Ganpat University, India)and Prachi Diwan (University of Hertfordshire, UK)
Copyright: 2026
Pages: 30
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.ch010
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Abstract
The medical imaging field is undergoing transformation through the integration of generative AI and explainable AI (XAI), enabling advanced diagnostics and transparent decision-making. This chapter explores the synergistic integration of these AI frameworks with metaheuristic algorithms, including Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and Differential Evolution, to enhance system performance and reliability. Metaheuristic approaches address optimization challenges while augmenting Variational Autoencoders and Generative Adversarial Networks in applications from synthetic image generation to rare pathology modeling. Case studies demonstrate how metaheuristic-optimized GANs improve image quality and address class imbalance, while metaheuristic algorithms enhance interpretability mechanisms including saliency maps, SHAP, and LIME, fostering trust in AI-driven diagnostics while ensuring regulatory compliance. This integration enables biomedical imaging systems to achieve superior performance, enhanced interpretability, and ethical implementation.
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