Brain Tumor MRI Image Segmentation Using Deep Learning Techniques - Image 1

£117.77

Academic Press Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Name: Brain Tumor MRI Image Segmentation Using Deep Learning Techniques
Brand: Academic Press
Price: 117.77 GBP
Availability: InStock

Biomedical Engineering

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Description

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. Review Offers a description of the deep learning approaches used for the segmentation of brain tumors, including modeling and properties From the Back Cover 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. About the Author 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).

Product Specifications

Brand: Academic Press
Format: Paperback
ASIN: 0323911714
Category: Books > Subjects > Health, Family & Lifestyle > Medical & Healthcare Practitioners > Allied Health Professions > Biomedical Engineering
Domain: Amazon UK
Release Date: 06 December 2021
Listed Since: 12 August 2021

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Academic Press Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

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