Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

1st Edition - November 27, 2021
Imprint: Academic Press
Editor: Jyotismita Chaki
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 1 7 1 - 9
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 8 3 9 5 - 2

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstra… Read more

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Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more.

The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation.

Cover Image
Title Page
Copyright
Table of Contents
Contributors
Chapter 1 Brain MRI segmentation using deep learning: background study and challenges
Abstract
1.1 Brain tumor and magnetic resonance imaging
1.2 Methods for brain MRI segmentation
1.3 Deep learning
1.4 Challenges of DL in the field of brain MRI segmentation
1.5 Summary
References
Chapter 2 Data preprocessing techniques for MRI brain scans using deep learning models
Abstract
2.1 Introduction
2.2 Related works
2.3 Traditional NLM algorithm
2.4 Proposed method
2.5 Material and metrics
2.6 Results and discussion
2.7 Conclusion
References
Chapter 3 A survey of brain segmentation methods from magnetic resonance imaging
Abstract
3.1 Introduction
3.2 Dataset
3.3 Methods
3.4 Challenges
3.5 Conclusion and discussion
References
Chapter 4 Brain tumor segmentation and detection in magnetic resonance imaging (MRI) using convolutional neural network
Abstract
4.1 Introduction
4.2 Literature survey
4.3 Proposed method
4.4 Data preprocessing
4.5 Splitting the preprocessed data
4.6 Building the CNN architecture
4.7 Fitting the CNN architecture
4.8 Results of the experiment
4.9 Discussions and conclusion
References
Chapter 5 Simultaneous brain tumor segmentation and molecular profiling using deep learning and T2w magnetic resonance images
Abstract
5.1 Introduction
5.2 Summary of the methods
5.3 Discussion
5.4 Conclusion
Acknowledgments
References
Chapter 6 An adaptive smart healthcare system to detect tumor from brain MRI using machine learning algorithm
Abstract
6.1 Introduction
6.2 Related work
6.3 Experimental dataset
6.4 Existing CAD system
6.5 Performance evaluation
6.6 Concluding remark
Conflict of interest
Funding Information
References
Chapter 7 Deep learning–based decision support system for multicerebral disease classification and identification
Abstract
7.1 Introduction
7.2 Interclassification of diseases
7.3 Performance
7.4 Proposed work
7.5 Conclusion and future work
References
Chapter 8 Multimodal MRI Brain Tumor Segmentation—A ResNet-based U-Net approach
Abstract
8.1 Introduction
8.2 Related works
8.3 Materials and metrics
8.4 Methodology
8.5 Results and discussions
8.6 Conclusion and future enhancements
References
Chapter 9 Deep learning-based brain malignant neoplasm classification using MRI image segmentation assisted by bias field correction and histogram equalization
Abstract
9.1 Overview of artificial intelligence
9.2 Activation functions in neural networks
9.3 Convolutional neural network
9.4 Proposed method
9.5 Results
9.6 Conclusion
References
Chapter 10 Brain MRI segmentation techniques based on CNN and its variants
Abstract
10.1 Introduction
10.2 Methodology
10.3 Neural networks, image classification, and related terminology
10.4 CNN architectures and their uses in brain imaging
10.5 Experiments performed and results
10.6 Conclusion
10.7 Future scope
References
Chapter 11 Detection of Brain Tumor with Magnetic Resonance Imaging using Deep Learning Techniques
Abstract
11.1 Introduction
11.2 Literature survey
11.3 Proposed method
11.4 Results and discussion
11.5 Conclusion
References
Chapter 12 On comparing optimizer of UNet-VGG16 architecture for brain tumor image segmentation
Abstract
12.1 Introduction
12.2 Methodology
12.3 Results and discussion
12.4 Conclusion
Reference
Chapter 13 Comparative analysis of deformable models based segmentation methods for brain tumor classification
Abstract
13.1 Introduction
13.2 Proposed methods with results
13.3 Conclusion
References
Chapter 14 Brain tumor segmentation using deep learning: taxonomy, survey and challenges
Abstract
14.1 Introduction
14.2 Previous works
14.3 Brain tumor
14.4 Brain tumor detection using image processing techniques
14.5 Brain tumor segmentation
14.6 Deep learning
14.7 Challenges
References
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

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Jyotismita Chaki

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