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Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Langue : Anglais

Coordonnateur : Chaki Jyotismita

Couverture de l’ouvrage Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

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.

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

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).
  • 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