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Brain Tumor MRI Image Segmentation Using Deep Learning Techniques
- 1st Edition - November 27, 2021
- 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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Request a sales quoteBrain 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.
- Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques
- Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more
- Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation
- Covers research Issues and the future of deep learning-based brain tumor segmentation
Academics (scientists, researchers, MSc. PhD. students) from the fields of Computer Science, Artificial Intelligence, Neural Engineering, and Information Technology. The audience also includes Neurologists who are interested in Deep Learning and Brain Tumor MRI Image Segmentation
1. Introduction to brain tumor segmentation using Deep Learning
2. Data preprocessing methods needed in brain tumor segmentation
3. Transformation of low-resolution brain tumor images into super-resolution images using Deep Learning based methods
4. Single path Convolutional Neural Network based brain tumor segmentation
5. Multi path Convolutional Neural Network based brain tumor segmentation
6. Fully Convolutional Networks (FCNs) based brain tumor segmentation
7. Cascade convolutional neural network-based brain tumor segmentation
8. Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) for brain tumor segmentation
9. Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN) for brain tumor segmentation
10. Generative Adversarial Networks (GAN) based brain tumor segmentation
11. Auto encoder-based brain tumor segmentation
12. Ensemble deep learning model-based brain tumor segmentation
13. Research Issues and Future of Deep Learning based brain tumor segmentation
- No. of pages: 258
- Language: English
- Edition: 1
- Published: November 27, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780323911719
- eBook ISBN: 9780323983952
JC
Jyotismita Chaki
Jyotismita Chaki, Ph.D., is an Assistant Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. She has done her PhD (Engg) from Jadavpur University, Kolkata, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Artificial Intelligence and Machine learning. She has authored more than forty international conferences and journal papers. She is the author and editor of more than five books. Currently she is the academic editor of PLOS ONE journal (IF: 3.24) and Associate editor of IET Image Processing Journal (IF: 2.373), Array journal (Elsevier) and Machine Learning with Applications journal (Elsevier).
Affiliations and expertise
Assistant Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India