Machine Learning in Medical Imaging and Computer Vision (Healthcare Technologies)

Cover
Contents
About the editors
Preface
1 Machine learning algorithms and applications in medical imaging processing
	1.1 Introduction
	1.2 Basic concepts
		1.2.1 Machine learning
		1.2.2 Stages for conducting machine learning
		1.2.3 Types of machine learning
	1.3 Proposed algorithm for supervised learning based on neuro-fuzzy system
		1.3.1 Input factors
		1.3.2 Output factors
	1.4 Application in medical images (numerical interpretation)
	1.5 Comparison of proposed approach with the existing approaches
	1.6 Conclusion
	References
2 Review of deep learning methods for medical segmentation tasks in brain tumors
	2.1 Introduction
	2.2 Brain segmentation dataset
		2.2.1 BraTS2012-2021
		2.2.2 MSD
		2.2.3 TCIA
	2.3 Brain tumor regional segmentation methods
		2.3.1 Fully supervised brain tumor segmentation
		2.3.2 Non-fully supervised brain tumor segmentation
		2.3.3 Summary
	2.4 Small sample size problems
		2.4.1 Class imbalance
		2.4.2 Data lack
		2.4.3 Missing modalities
		2.4.4 Summary
	2.5 Model interpretability
	2.6 Conclusion and outlook
	References
3 Optimization algorithms and regularization techniques using deep learning
	3.1 Introduction
	3.2 Deep learning approaches
		3.2.1 Deep supervised learning
		3.2.2 Deep semi-supervised learning
		3.2.3 Deep unsupervised learning
		3.2.4 Deep reinforcement learning
	3.3 Deep neural network
		3.3.1 Recursive neural network
		3.3.2 Recurrent neural network
		3.3.3 Convolutional neural network
	3.4 Optimization algorithms
		3.4.1 Gradient descent
		3.4.2 Stochastic gradient descent
		3.4.3 Mini-batch-stochastic gradient descent
		3.4.4 Momentum
		3.4.5 Nesterov momentum
		3.4.6 Adapted gradient (AdaGrad)
		3.4.7 Adapted delta (AdaDelta)
		3.4.8 Root mean square propagation
		3.4.9 Adaptive moment estimation (Adam)
		3.4.10 Nesterov-accelerated adaptive moment (Nadam)
		3.4.11 AdaBelief
	3.5 Regularizations techniques
		3.5.1 l2 Regularization
		3.5.2 l1 Regularization
		3.5.3 Entropy regularization
		3.5.4 Dropout technique
	3.6 Review of literature
	3.7 Deep learning-based neuro fuzzy system and its applicability in self-driven cars in hill stations
	3.8 Conclusion
	References
4 Computer-aided diagnosis in maritime healthcare: review of spinal hernia
	4.1 Introduction
	4.2 Similar studies and common diseases of the seafarers
	4.3 Background
	4.4 Computer-aided diagnosis of spinal hernia
	4.5 Conclusion
	References
5 Diabetic retinopathy detection using AI
	5.1 Introduction
	5.2 Methodology
		5.2.1 Preprocessing
		5.2.2 Feature extraction
		5.2.3 Classification
		5.2.4 Proposed method algorithm
		5.2.5 Training and testing
		5.2.6 Novel ISVM-RBF
	5.3 Results and discussion
		5.3.1 Dataset
		5.3.2 Image processing results
		5.3.3 Comparison with the state-of-the-art studies
	5.4 Conclusion
	Funding
	References
6 A survey image classification using convolutional neural network in deep learning
	6.1 Introduction
	6.2 Deep learning
		6.2.1 Artificial neural network
		6.2.2 Recurrent neural network
		6.2.3 Feed forward neural network
	6.3 Convolutional neural network
		6.3.1 Convolutional layer
		6.3.2 Pooling layer
		6.3.3 Fully connected layer
		6.3.4 Dropout layer
		6.3.5 Softmax layer
	6.4 CNN models
		6.4.1 VGGnet
		6.4.2 AlexNet
		6.4.3 GoogleNet
		6.4.4 DenseNet
		6.4.5 MobileNet
		6.4.6 ResNet
		6.4.7 NasNet
		6.4.8 ImageNet
	6.5 Image classification
	6.6 Literature survey
	6.7 Discussion
	6.8 Conclusion
	References
7 Text recognition using CRNN models based on temporal classification and interpolation methods
	7.1 Introduction
	7.2 Related works
	7.3 Datasets
	7.4 Model and evaluation matrix
		7.4.1 Process of data pre-processing
		7.4.2 Air-writing recognition (writing in air)
	7.5 Description and working of the model
		7.5.1 Handwritten text recognition
	7.6 Convolutional neural network
	7.7 Connectionist temporal classification
	7.8 Decoding
	7.9 Optimal fixed length
	7.10 Using different interpolation techniques for finding the ideal fixed frame length signals
	7.11 CNN architecture
	7.12 Evaluation matrix
		7.12.1 Handwritten text recognition
		7.12.2 Air-writing recognition
	7.13 Results and discussion
		7.13.1 Handwritten text recognition
		7.13.2 Air-writing recognition
	7.14 Conclusion
	References
8 Microscopic Plasmodium classification (MPC) using robust deep learning strategies for malaria detection
	8.1 Introduction
		8.1.1 Classification of
		using CNN
	8.2 Related works
	8.3 Methodology
		8.3.1 Data preprocessing
		8.3.2 Data augmentation
		8.3.3 Weight regularization using batch normalization
		8.3.4 Classification based on pattern recognition
		8.3.5 Models for multi-class classification
	8.4 Experimental results and discussion
		8.4.1 Dataset description
		8.4.2 Performance measures
	8.5 Conclusion and future work
	References
9 Medical image classification and retrieval using deep learning
	9.1 Medical images
		9.1.1 Ultrasound images
		9.1.2 Magnetic resonance imaging
		9.1.3 X-ray imaging for pediatric
		9.1.4 X-ray imaging for medical
	9.2 Deep learning
		9.2.1 Feed-forward neural networks
		9.2.2 Recurrent neural networks
		9.2.3 Convolutional neural networks
	9.3 Deep learning applications in medical images
		9.3.1 Identification of anatomical structures
		9.3.2 Deep-learning-based organs and cell identification
		9.3.3 Deep learning for cell detection
	9.4 Deep learning for segmentation
	9.5 Conclusion
	References
10 Game theory, optimization algorithms and regularization techniques using deep learning in medical imaging
	10.1 Introduction
	10.2 Game theoretical aspects in MI
		10.2.1 Cooperative games
		10.2.2 Competitive games
		10.2.3 Zero-sum and non-zero-sum games
		10.2.4 Deep learning in game theory
	10.3 Optimization techniques in MI
		10.3.1 Linear programming
		10.3.2 Nonlinear programming
		10.3.3 Dynamical programming
		10.3.4 Particle swarm optimization
		10.3.5 Simulated annealing algorithm
		10.3.6 Genetic algorithm
	10.4 Regularization techniques in MI
	10.5 Remarks and future directions
	10.6 Conclusion
	References
11 Data preparation for artificial intelligence in
federated learning: the influence of artifacts on
the composition of the mammography database
	11.1 Introduction
	11.2 Federate learning
	11.3 Methodology
	11.4 Results
		11.4.1 Discussion
	11.5 Conclusions
	References
12 Spatial cognition by the visually impaired: image
processing with SIFT/BRISK-like detector and
two-keypoint descriptor on Android CameraX
	12.1 Introduction
		12.1.1 Contribution
	12.2 Related work
	12.3 Methodology
		12.3.1 Problem formulation
		12.3.2 Identification of all keypoints on the template image: SIFT-like approach
		12.3.3 Identification of two keypoints to design the template image feature descriptor: BRISK-like approach
		12.3.4 Fast binary feature matching
	12.4 Implementation, results, and discussion
		12.4.1 Implementation
		12.4.2 Results and discussion
	12.5 Conclusions
	Abbreviations
	Data availability
	Conflicts of interest
	Acknowledgments
	References
13 Feature extraction process through hypergraph learning with the concept of rough set classification
	13.1 Introduction
	13.2 Rough set theory
		13.2.1 Preliminaries
	13.3 Rough graph
	13.4 Proposed work
		13.4.1 Rough hypergraph
		13.4.2 Methodology
		13.4.3 Experimental results
	13.5 Results and discussion
	References
14 Machine learning for neurodegenerative disease diagnosis: a focus on amyotrophic lateral sclerosis (ALS)
	14.1 Introduction
	14.2 Neurodegenerative diseases
		14.2.1 Alzheimer’s disease
		14.2.2 Parkinson’s disease
		14.2.3 Huntington’s disease
		14.2.4 Amyotrophic lateral sclerosis
	14.3 The development stages of NDDs
	14.4 Neuroimages on neurodegenerative diseases
		14.4.1 Structural magnetic resonance
		14.4.2 Diffusion tensor imaging
		14.4.3 Functional magnetic resonance imaging
	14.5 Machine learning and deep learning applications on ALS
	14.6 Proposed research methodology
		14.6.1 Methodology flow
		14.6.2 Approaches to predictive machine learning
		14.6.3 Discussion on review findings
	14.7 Conclusion and future work
	References
15 Using deep/machine learning to identify patterns and detecting misinformation for pandemics in the post-COVID-19 era
	15.1 Introduction
	15.2 Literature review
		15.2.1 Difference between misinformation and disinformation
		15.2.2 Detection of fake news
	15.3 Proposed approach
		15.3.1 Neural networks
		15.3.2 Convolutional neural network
		15.3.3 Recurrent neural network
		15.3.4 Random forest
		15.3.5 Hybrid CNN-RNN-RF model
	15.4 Methodology
		15.4.1 Datasets
		15.4.2 Data-cleaning
		15.4.3 Feature extraction method
	15.5 Proposed method
	15.6 Comparison of models
		15.6.1 Hyperparameter optimization method
		15.6.2 Evaluation benchmarks
	15.7 Future work
	15.8 Conclusion
	References
16 Integrating medical imaging using analytic modules and applications
	16.1 Introduction
	16.2 Applications of medical imaging
		16.2.1 Radiology and diagnostic imaging
		16.2.2 Pathology
		16.2.3 Cardiology
		16.2.4 Neuroimaging
		16.2.5 Ophthalmology imaging
	16.3 Key aspects of integrating medical imaging
		16.3.1 Interoperability
		16.3.2 Picture archiving and communication system
		16.3.3 Electronic health records
		16.3.4 Decision support systems
		16.3.5 Telemedicine and remote access
		16.3.6 Clinical workflow optimization
	16.4 Analytic modules in integrating medical imaging
		16.4.1 Image segmentation
		16.4.2 Registration and fusion
		16.4.3 Quantitative analysis
		16.4.4 Texture analysis
		16.4.5 Deep learning and AI algorithms
		16.4.6 Visualization and 3D reconstruction
	16.5 Algorithm for integrating medical imaging using analytic modules
		16.5.1 Data preprocessing algorithms
		16.5.2 Segmentation algorithms
		16.5.3 Feature extraction methods
		16.5.4 Machine learning techniques
		16.5.5 Fusion algorithms
		16.5.6 Decision support algorithms
		16.5.7 Visualization algorithms
	16.6 Conclusion
	References
Index
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