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. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front Matter - p. Page iv 6. Copyright - p. Page v 7. Contents - p. Page vi 8. Contributors - p. Page vii 9. List of Illustrations - p. Page viii 10. List of Tables - p. Page ix 11. 1 : Brain MRI segmentation using deep learning: background study and challenges - p. Page ix 12. 2 : Data preprocessing techniques for MRI brain scans using deep learning models - p. Page ix 13. 3 : A survey of brain segmentation methods from magnetic resonance imaging - p. Page ix 14. 4 : Brain tumor segmentation and detection in magnetic resonance imaging (MRI) using convolutional neural network - p. Page ix 15. 5 : Simultaneous brain tumor segmentation and molecular profiling using deep learning and T2w magnetic resonance images - p. Page ix 16. 6 : An adaptive smart healthcare system to detect tumor from brain MRI using machine learning algorithm - p. Page ix 17. 7 : Deep learning–based decision support system for multicerebral disease classification and identification - p. Page ix 18. 8 : Multimodal MRI Brain Tumor Segmentation—A ResNet-based U-Net approach - p. Page ix 19. 9 : Deep learning-based brain malignant neoplasm classification using MRI image segmentation assisted by bias field correction and histogram equalization - p. Page ix 20. 10 : Brain MRI segmentation techniques based on CNN and its variants - p. Page ix 21. 11 : Detection of Brain Tumor with Magnetic Resonance Imaging using Deep Learning Techniques - p. Page ix 22. 12 : On comparing optimizer of UNet-VGG16 architecture for brain tumor image segmentation - p. Page ix 23. 13 : Comparative analysis of deformable models based segmentation methods for brain tumor classification - p. Page ix 24. 14 : Brain tumor segmentation using deep learning: taxonomy, survey and challenges - p. Page ix 25. Index - p. Page ix
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. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Front Matter - p. Page iv 6. Copyright - p. Page v 7. Contents - p. Page vi 8. Contributors - p. Page vii 9. List of Illustrations - p. Page viii 10. List of Tables - p. Page ix 11. 1 : Brain MRI segmentation using deep learning: background study and challenges - p. Page ix 12. 2 : Data preprocessing techniques for MRI brain scans using deep learning models - p. Page ix 13. 3 : A survey of brain segmentation methods from magnetic resonance imaging - p. Page ix 14. 4 : Brain tumor segmentation and detection in magnetic resonance imaging (MRI) using convolutional neural network - p. Page ix 15. 5 : Simultaneous brain tumor segmentation and molecular profiling using deep learning and T2w magnetic resonance images - p. Page ix 16. 6 : An adaptive smart healthcare system to detect tumor from brain MRI using machine learning algorithm - p. Page ix 17. 7 : Deep learning–based decision support system for multicerebral disease classification and identification - p. Page ix 18. 8 : Multimodal MRI Brain Tumor Segmentation—A ResNet-based U-Net approach - p. Page ix 19. 9 : Deep learning-based brain malignant neoplasm classification using MRI image segmentation assisted by bias field correction and histogram equalization - p. Page ix 20. 10 : Brain MRI segmentation techniques based on CNN and its variants - p. Page ix 21. 11 : Detection of Brain Tumor with Magnetic Resonance Imaging using Deep Learning Techniques - p. Page ix 22. 12 : On comparing optimizer of UNet-VGG16 architecture for brain tumor image segmentation - p. Page ix 23. 13 : Comparative analysis of deformable models based segmentation methods for brain tumor classification - p. Page ix 24. 14 : Brain tumor segmentation using deep learning: taxonomy, survey and challenges - p. Page ix 25. Index - p. Page ix
