Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a comprehensive exploration of cutting-edge deep learning approaches for effective brain tumor segmentation. This insightful academic resource elucidates the core concepts of deep learning algorithms through engaging diagrams, data tables, and examples, making complex ideas accessible and practical.

This book begins by introducing foundational concepts of deep learning-based brain tumor segmentation, followed by detailed sections on modeling, segmentation, and properties. A particular emphasis is placed on the application of various convolutional neural networks, such as single path, multi path, fully convolutional networks, cascade convolutional neural networks, Long Short-Term Memory – Recurrent Neural Networks (LSTM-RNN), and Gated Recurrent Units (GRU), among others.

The book also addresses how leveraging deep neural networks can tackle new questions, enhance protocols, and overcome existing challenges in the realm of brain tumor segmentation.

• Provides readers with a solid understanding of deep learning-based approaches in brain tumor segmentation, including essential preprocessing techniques.
• Integrates recent advancements such as transforming low-resolution brain tumor images into super-resolution images using deep learning, and explores techniques such as single path Convolutional Neural Network-based segmentation and more.

Authors:
Jyotismita Chaki (Editor)
Edition:
1st
Publication Date:
December 16, 2021