Unlock the Power of Deep Learning in Brain Tumor MRI Image Segmentation

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques, authored by Jyotismita Chaki, is a comprehensive guide to deep learning approaches for brain tumor segmentation. This book provides an in-depth exploration of the core concepts of deep learning algorithms, using diagrams, data tables, and examples to illustrate brain tumor segmentation.

Understanding Deep Learning-Based Brain Tumor Segmentation

The book introduces readers to the fundamental concepts of deep learning-based brain tumor segmentation, followed by sections on techniques for modeling, segmentation, and properties. The author places a strong emphasis on the application of different types of convolutional neural networks, including:

• Single path Convolutional Neural Networks
• Multi-path Convolutional Neural Networks
• Fully Convolutional Networks
• Cascade Convolutional Neural Networks
• Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN)
• Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN)

Advancements in Brain Tumor Segmentation

The book highlights recent advancements in the field, including:

• 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
• Generative Adversarial Networks (GAN) for brain tumor segmentation
• Autoencoder-based brain tumor segmentation
• Ensemble deep learning Model-based brain tumor segmentation

Research Issues and Future Directions

The book discusses research issues and future directions in deep learning-based brain tumor segmentation, making it an invaluable resource for researchers, students, and professionals in the field.

Book Details

Publisher: Elsevier Science
Publication Date: November 27, 2021
Language: English
ISBN: 9780323911719, 9780323983952