Metaheuristics Algorithms for Medical Applications
Methods and Applications
- 1st Edition - November 24, 2023
- Imprint: Academic Press
- Authors: Mohamed Abdel-Basset, Reda Mohamed, Mohamed Elhoseny
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 3 1 4 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 3 1 5 - 2
Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with ma… Read more
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Request a sales quoteMetaheuristics 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.
- Introduces a new set of metaheuristics techniques for biomedical applications
- Presents basic concepts of metaheuristics, methods and practices, followed by advanced topics and applications
- Provides researchers, practitioners, and project stakeholders with a complete guide for understanding and applying metaheuristics and machine learning techniques in their projects and solutions
The primary audience includes Computer Scientists and researchers in Artificial Intelligence and Machine Learning, specifically in the field of developing applied Metaheursitics algorithms for biomedical applications. As such, clinicians, biomedical engineers, and biomedical researchers will also be a primary audience for the book.
- Cover image
- Title page
- Table of Contents
- Copyright
- 1. Metaheuristic algorithms and medical applications
- Abstract
- 1.1 Introduction
- 1.2 What is the optimization problem
- 1.3 Optimization problems in medical applications
- 1.4 What is metaheuristics
- 1.5 Chapter summary
- References
- 2. Wavelet-based image denoising using improved artificial jellyfish search optimizer
- Abstract
- 2.1 Introduction
- 2.2 Wavelet denoising
- 2.3 Artificial jellyfish search optimizer
- 2.4 How to estimate the wavelet coefficients
- 2.5 Experimental settings
- 2.6 Performance metrics
- 2.7 Practical analysis
- 2.8 Chapter summary
- References
- 3. Artificial gorilla troops optimizer for human activity recognition in IoT-based medical applications
- Abstract
- 3.1 Introduction
- 3.2 Methods
- 3.3 Metaheuristics-based DNN’s hyperparameters tuning
- 3.4 Dataset description and experiment settings
- 3.5 Results and discussion
- 3.6 Chapter summary
- References
- 4. Improved gradient-based optimizer for medical image enhancement
- Abstract
- 4.1 Introduction
- 4.2 Methods
- 4.3 Metaheuristics-based image enhancement technique
- 4.4 Practical analysis
- 4.5 Chapter summary
- References
- 5. Metaheuristic-based multilevel thresholding segmentation technique for brain magnetic resonance images
- Abstract
- 5.1 Introduction
- 5.2 Techniques for image segmentation
- 5.3 Problem formulation
- 5.4 How to implement a metaheuristic for the MISP
- 5.5 Practical analysis
- 5.6 Chapter summary
- References
- 6. Metaheuristic algorithm’s role for machine learning techniques in medical applications
- Abstract
- 6.1 Introduction
- 6.2 Support vector machine
- 6.3 K-nearest neighbor algorithm
- 6.4 Naive Bayes algorithm
- 6.5 Random forest
- 6.6 K-means clustering algorithm
- 6.7 Multilayer perceptron
- 6.8 Decision tree induction
- 6.9 Logistic regression
- 6.10 Chapter summary
- References
- 7. Metaheuristic algorithms collaborated with various machine learning models for feature selection in medical data: Comparison and analysis
- Abstract
- 7.1 Introduction
- 7.2 Feature selection techniques
- 7.3 Wrapper-based methods
- 7.4 Experiment settings
- 7.5 Performance metrics
- 7.6 Practical analysis
- 7.7 Chapter summary
- References
- 8. Machine learning and improved multiobjective binary generalized normal distribution optimization in feature selection for cancer classification
- Abstract
- 8.1 Introduction
- 8.2 Background
- 8.3 Multiobjective improved binary GNDO
- 8.4 Practical analysis
- 8.5 Chapter summary
- References
- 9. Metaheuristics for assisting the deep neural network in classifying the chest X-ray images infected with COVID-19
- Abstract
- 9.1 Introduction
- 9.2 Deep learning techniques for COVID-19 diagnosis
- 9.3 Metaheuristics for COVID-19 diagnosis
- 9.4 Metaheuristics-assisted deep neural network for COVID-19 diagnosis
- 9.5 Dataset description
- 9.6 Preprocessing step
- 9.7 Experimental settings
- 9.8 Practical findings
- 9.9 Chapter summary
- References
- 10. Metaheuristic algorithms for multimodal image fusion of magnetic resonance and computed tomography brain tumor images: a comparative study
- Abstract
- 10.1 Introduction
- 10.2 Discrete wavelet transform
- 10.3 Image fusion rule
- 10.4 Seagull optimization algorithm
- 10.5 Proposed algorithm for multimodal medical image fusion problem
- 10.6 Performance metrics
- 10.7 Practical analysis
- 10.8 Chapter summary
- References
- 11. Metaheuristic algorithms for medical image registration: a comparative study
- Abstract
- 11.1 Introduction
- 11.2 Techniques for image registration
- 11.3 Artificial gorilla troops optimizer
- 11.4 Marine predators algorithm
- 11.5 Proposed algorithms for image registration
- 11.6 Practical analysis
- 11.7 Chapter summary
- References
- 12. Challenges, opportunities, and future prospects
- Abstract
- 12.1 Introduction
- 12.2 Challenges
- 12.3 Future directions
- References
- Index
- Edition: 1
- Published: November 24, 2023
- No. of pages (Paperback): 248
- No. of pages (eBook): 248
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443133145
- eBook ISBN: 9780443133152
MA
Mohamed Abdel-Basset
Dr. Mohamed Abdel-Basset is Associate Professor and Head of the Department of Computer Science, within the Faculty of Computers and Informatics, at Zagazig University, Egypt. He received his B.Sc., M.Sc and Ph.D in operations research at the Faculty of Computers and Informatics, Zagazig University. Dr. Abdel-Basset’s research interests are in Optimization, Operations Research, Data Mining, Computational Intelligence, Applied Statistics, Decision Support Systems, Robust Optimization, Engineering Optimization, Multiobjective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is currently working on the application of multi-objective and robust meta-heuristic optimization techniques. Dr. Abdel-Basset is an Editor or Reviewer for several international journals and conferences, and has published more than 100 articles in international journals and conference proceedings.
Affiliations and expertise
Associate Professor, Faculty of Computers and Informatics, Zagazig University, EgyptRM
Reda Mohamed
Dr. Reda Mohamed is a Senior Researcher in the Faculty of Computers and Informatics at Zagazig University, Egypt. His research interests include Optimization, Deep Learning algorithms, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He has published more than 70 articles in international journals and conference proceedings. He is currently working on the application of multi-objective and robust meta-heuristic optimization techniques.
Affiliations and expertise
Senior Researcher, Faculty of Computers and Informatics, Zagazig University, EgyptME
Mohamed Elhoseny
Dr. Mohamed Elhoseny is an Associate Professor at the University of Sharjah, UAE. Dr. Elhoseny is an ACM Distinguished Speaker and IEEE Senior Member. His research interests include Smart Cities, Network Security, Artificial Intelligence, Internet of Things, and Intelligent Systems. Dr. Elhoseny is the founder and the Editor-in-Chief of the IJSSTA journal published by IGI Global, as well as Associate Editor at several Q1 journals such as IEEE Access, Scientific Reports, IEEE Future Directions, Remote Sensing, International Journal of E-services and Mobile Applications and Human-centric Computing and Information Sciences. He has also served as the co-chair, publication chair, program chair, and a track chair for several international conferences published by recognized publishers. Dr. Elhoseny is Editor-in-Chief of two book series, on Sensor Communication for Urban Intelligence and Distributed Sensing and Intelligent Systems.
Affiliations and expertise
Associate Professor, University of Sharjah, UAERead Metaheuristics Algorithms for Medical Applications on ScienceDirect