Metaheuristic Algorithms And Optimizing Neural Networks For Biomedical Image Processing
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Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Author: Prasanalakshmi Balaji
language: en
Publisher: Medical Information Science Reference
Release Date: 2025-06-06
Metaheuristic algorithms emerge as powerful tools for optimizing complex systems, particularly in neural networks, where traditional methods may cause challenges. In biomedical image processing, the integration of metaheuristics like genetic algorithms, particle swarm optimization, and differential evolution offers promising improvements in neural network performance. These algorithms help improve hyperparameters and optimize architectures, enhancing the accuracy of tasks like disease detection, image segmentation, and classification. Further research into this convergence between metaheuristic optimization and deep learning may help advance medical diagnostics and healthcare technologies. Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing explores the optimization of neural networks for biomedical image analysis. It provides valuable insights into advanced image processing for improved healthcare, advanced technology, and potential scientific and computational breakthroughs. This book covers topics such as medical imaging, genetics, and psychology, and is a useful resource for business owners, computer engineers, medical professionals, academicians, researchers, and data scientists.
Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Metaheuristic algorithms emerge as powerful tools for optimizing complex systems, particularly in neural networks, where traditional methods may cause challenges. In biomedical image processing, the integration of metaheuristics like genetic algorithms, particle swarm optimization, and differential evolution offers promising improvements in neural network performance. These algorithms help improve hyperparameters and optimize architectures, enhancing the accuracy of tasks like disease detection, image segmentation, and classification. Further research into this convergence between metaheuristic optimization and deep learning may help advance medical diagnostics and healthcare technologies. Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing explores the optimization of neural networks for biomedical image analysis. It provides valuable insights into advanced image processing for improved healthcare, advanced technology, and potential scientific and computational breakthroughs. This book covers topics such as medical imaging, genetics, and psychology, and is a useful resource for business owners, computer engineers, medical professionals, academicians, researchers, and data scientists.
Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions
This book is an authoritative compilation of the latest advancements in optimization techniques. This book covers a wide array of methods ranging from classical to metaheuristic to AI-enhanced approaches. The chapters are meticulously selected and organized in three sections—metaheuristics, machine learning and engineering applications. This allows for an in-depth exploration of diverse topics ranging from image processing to feature selection to data clustering, to practical applications like energy optimization, smart grids, healthcare diagnostics, etc. Each chapter delves into the specific algorithms and applications as well as provides ample theoretical insights. Accordingly, this book is ideally suited for undergraduate and postgraduate students in fields such as science, engineering and computational mathematics. It is also an invaluable resource for courses on artificial intelligence, computational intelligence, etc. Researchers and professionals in evolutionary computation, artificial intelligence and engineering will find the material especially useful for advancing their work and exploring new frontiers in optimization.