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Metaheuristics algorithms for medical applications : methods and applications /

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.

Bibliographic Details
Main Author: Abdel-Basset, Mohamed
Corporate Author: ScienceDirect (Online service)
Other Authors: Mohamed, Reda, Elhoseny, Mohamed
Format: eBook
Language:English
Published: London : Academic Press, 2024.
Subjects:
Metaheuristics.
Artificial intelligence > Medical applications.
Métaheuristiques.
Intelligence artificielle > Applications en médecine.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Metaheuristics Algorithms for Medical Applications
  • Copyright Page
  • Contents
  • 1 Metaheuristic algorithms and medical applications
  • 1.1 Introduction
  • 1.2 What is the optimization problem
  • 1.3 Optimization problems in medical applications
  • 1.4 What is metaheuristics
  • 1.4.1 Metaheuristics classification
  • 1.4.2 Main stages of a metaheuristic
  • 1.4.3 Nutcracker optimization algorithm
  • 1.4.4 Teaching-learning-based optimization
  • 1.4.4.1 Teacher phase
  • 1.4.4.2 Learner phase
  • 1.4.5 Differential evolution
  • 1.4.5.1 Mutation operator
  • 1.4.5.2 Crossover operator
  • 1.4.5.3 Selection operator
  • 1.4.6 Light spectrum optimizer
  • 1.4.7 Exploration mechanism
  • 1.4.7.1 Exploitation mechanism
  • 1.5 Chapter summary
  • References
  • 2 Wavelet-based image denoising using improved artificial jellyfish search optimizer
  • 2.1 Introduction
  • 2.2 Wavelet denoising
  • 2.2.1 Wavelet transform
  • 2.2.2 Principle of wavelet denoising
  • 2.3 Artificial jellyfish search optimizer
  • 2.4 How to estimate the wavelet coefficients
  • 2.4.1 Initialization
  • 2.4.2 Objective function
  • 2.4.3 Improved JS
  • 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
  • 3.1 Introduction
  • 3.2 Methods
  • 3.2.1 Deep neural network
  • 3.2.2 Artificial gorilla troops optimizer
  • 3.2.3 Grey wolf optimizer
  • 3.3 Metaheuristics-based DNN's hyperparameters tuning
  • 3.3.1 Initialization
  • 3.3.2 Constructing DNN
  • 3.3.3 Evaluation
  • 3.3.3.1 Performance metrics
  • 3.3.3.2 Dataset preprocessing
  • 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.
  • 4.1 Introduction
  • 4.2 Methods
  • 4.2.1 Transformation function
  • 4.2.2 Objective function
  • 4.2.3 Gradient-based optimizer
  • 4.2.3.1 Gradient search rule phase
  • 4.2.3.2 Local escaping operator (LEO) phase
  • 4.3 Metaheuristics-based image enhancement technique
  • 4.3.1 Step 1: initialization
  • 4.3.2 Step 2: novel self-adaptive strategy (SAS)
  • 4.3.3 Step 3: evaluation stage
  • 4.3.4 Step 5: pseudocode of the proposed IGNDO
  • 4.4 Practical analysis
  • 4.5 Chapter summary
  • References
  • 5 Metaheuristic-based multilevel thresholding segmentation technique for brain magnetic resonance images
  • 5.1 Introduction
  • 5.2 Techniques for image segmentation
  • 5.3 Problem formulation
  • 5.3.1 Kapur's entropy
  • 5.3.2 Otsu method
  • 5.4 How to implement a metaheuristic for the MISP
  • 5.5 Practical analysis
  • 5.5.1 Evaluation using SD metric
  • 5.5.2 Comparison under fitness values
  • 5.5.3 Comparison under PSNR values
  • 5.5.4 Comparison under SSIM values
  • 5.5.5 Comparison under FSIM values
  • 5.5.6 Computational cost analysis
  • 5.6 Chapter summary
  • References
  • 6 Metaheuristic algorithm's role for machine learning techniques in medical applications
  • 6.1 Introduction
  • 6.2 Support vector machine
  • 6.3 K-nearest neighbor algorithm
  • 6.3.1 Weighted KNN (wKNN) algorithm
  • 6.4 Naive Bayes algorithm
  • 6.4.1 Why is this algorithm known as naive Bayes?
  • 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: Compari...
  • 7.1 Introduction
  • 7.2 Feature selection techniques
  • 7.2.1 Filter methods
  • 7.2.2 Chi-square feature selection
  • 7.2.3 Classical Fisher score
  • 7.2.4 Generalized Fisher score.
  • 7.2.5 Correlation criteria
  • 7.2.6 Mutual information
  • 7.3 Wrapper-based methods
  • 7.3.1 Sequential selection algorithms
  • 7.3.2 Metaheuristic-based feature selection
  • 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...
  • 8.1 Introduction
  • 8.2 Background
  • 8.2.1 Multiobjective optimization
  • 8.2.2 Generalized normal distribution optimization
  • 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
  • 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...
  • 10.1 Introduction
  • 10.2 Discrete wavelet transform
  • 10.3 Image fusion rule
  • 10.4 Seagull optimization algorithm
  • 10.4.1 Migration behavior: exploration operator
  • 10.4.2 Attacking behavior: exploitation operator
  • 10.5 Proposed algorithm for multimodal medical image fusion problem
  • 10.5.1 Initialization
  • 10.5.2 Root mean squared error
  • 10.5.3 Improved seagull optimization algorithm
  • 10.5.4 Hybridization between TLBO and ISOA
  • 10.6 Performance metrics
  • 10.7 Practical analysis
  • 10.8 Chapter summary
  • References
  • 11 Metaheuristic algorithms for medical image registration: a comparative study.
  • 11.1 Introduction
  • 11.2 Techniques for image registration
  • 11.2.1 Feature-based image registration techniques
  • 11.2.2 Intensity-based image registration techniques
  • 11.3 Artificial gorilla troops optimizer
  • 11.3.1 Exploration operator
  • 11.3.2 Exploitation operator
  • 11.4 Marine predators algorithm
  • 11.5 Proposed algorithms for image registration
  • 11.5.1 Initialization
  • 11.5.2 Evaluation step
  • 11.5.3 Convergence improvement strategy
  • 11.5.4 Pseudocode of a studied algorithm
  • 11.6 Practical analysis
  • 11.7 Chapter summary
  • References
  • 12 Challenges, opportunities, and future prospects
  • 12.1 Introduction
  • 12.2 Challenges
  • 12.3 Future directions
  • References
  • Index
  • Back Cover.