| Description |
1 online resource (xviii, 362 pages) : illustrations |
| Series |
Healthcare technologies series ; 49 |
|
Healthcare technologies series ; 49.
|
| Contents |
Intro -- Title -- Copyright -- 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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses |
| Bibliography |
Includes bibliographical references and index |
| Notes |
5.3.3 Comparison with the state-of-the-art studies |
|
Description based on online resource; title from PDF title page (IET Digital Library, viewed on January 15, 2024) |
| Subject |
Medical informatics.
|
|
Diagnostic imaging.
|
|
Machine learning -- Industrial applications
|
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Artificial intelligence -- Medical applications.
|
|
Diagnostic Imaging
|
| Form |
Electronic book
|
| Author |
Nandal, Amita, editor.
|
|
Zhou, Liang, editor
|
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Dhaka, Arvind, editor
|
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Ganchev, Todor, editor.
|
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Nait-Abdesselam, Farid, editor
|
| ISBN |
9781839535949 |
|
1839535946 |
|
