Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with machine learning for solving biomedical problems. This book is organized to present a stepwise progression beginning with the basics of metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of this book presents the fundamental concepts of metaheuristics and machine learning and provides a comprehensive taxonomic view of metaheuristics methods according to a variety of criteria such as data type, scope, and method. The second section of this book explains how to apply metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in metaheuristics for biomedical science. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.
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Table of Contents
1. Metaheuristic algorithms and medical applications2. Wavelet-based image denoising using improved artificial jellyfish search optimizer
3. Artificial gorilla troops optimizer for human activity recognition in IoT-based medical applications
4. Improved gradient-based optimizer for medical image enhancement
5. Metaheuristic-based multilevel thresholding segmentation technique for brain magnetic resonance images
6. Metaheuristic algorithm's role for machine learning techniques in medical applications
7. Metaheuristic algorithms collaborated with various machine learning models for feature selection in medical data: Comparison and analysis
8. Machine learning and improved multiobjective binary generalized normal distribution optimization in feature selection for cancer classification
9. Metaheuristics for assisting the deep neural network in classifying the chest X-ray images infected with COVID-19
10. Metaheuristic algorithms for multimodal image fusion of magnetic resonance and computed tomography brain tumor images: a comparative study
11. Metaheuristic algorithms for medical image registration: a comparative study
12. Challenges, opportunities, and future prospects
