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Artificial Intelligence Strategies for Brain Tumor Diagnosis
Postoperative Analysis
- 1st Edition - January 1, 2029
- Editors: Ayman S. El-Baz, Jasjit Suri
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 2 9 5 - 4
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 2 9 6 - 1
Artificial Intelligence Strategies for Brain Tumor Diagnosis: Postoperative Analysis presents the latest advances in Artificial Intelligence applied to clinical diagnosis and tr… Read more
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Request a sales quoteArtificial Intelligence Strategies for Brain Tumor Diagnosis: Postoperative Analysis presents the latest advances in Artificial Intelligence applied to clinical diagnosis and treatment of Brain Tumors. Advances in the role of segmentation, classification, and postoperative analysis are covered, including relevant case studies. For patients with a brain tumor, the first step in treatment is often surgery to remove as much of the mass as possible. A tumor sample obtained and analyzed during surgery can help to precisely diagnose the tumor and define the margins between tumor and healthy brain tissue. In this book, the authors present an overview of different Artificial Intelligent techniques used in the brain care domain including classification, segmentation, organizational preparation, postoperative analysis, and predictive methods. Furthermore, the authors provide a thorough description of recent classification methods based on brain connectivity. Taking into account recent developments and the rapidly growing potential of the field, the authors discuss how AI might transform brain care in the near and long term, identifying open issues and promising directions for future work. Artificial Intelligence Strategies for Brain Tumor Diagnosis: Postoperative Analysis includes three Parts: Brain Tumor Segmentation, Brain Tumor Classification, and Postoperative Analysis of Brain Tumors.
- Presents Artificial Intelligence techniques applied to diagnosis and postoperative analysis of brain tumors
- Includes in-depth technical coverage of segmentation techniques, including coverage of convolutional neural networks, multi-spectral brain tumor segmentation, deep neural networks, conditional random fields, and support vector machines
- Covers brain tumor classification using AI, Deep Learning, GA-SVM, MRI adaboost, and efficient deep features of MRI scans
- Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of AI for diagnosis and postoperative analysis of brain tumors
Biomedical Engineers and researchers in Neural Engineering, medical imaging, and neural networks, radiologists, neurologists, neurosurgeons, AI researchers, and designers of Machine Learning applications, Physicists specializing in medical imaging, oncologists, pathologists, microbiologists
Section I: Brian Tumor Segmentation 1: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks2: Deep learning-based model for brain tumor segmentation3: Brain tumor segmentation with deep neural networks4: An efficient brain tumor detection algorithm using LCDG5: Segmenting brain tumors with conditional random fields and support vector machinesSection II: Brain Tumor Classification 6: Brain tumor classification using artificial intelligence paradigm7: Brain tumor classification using Deep learning network8: Multiclass brain tumor classification using GA-SVM9: MR image classification using adaboost for brain tumor10: Investigating machine learning techniques for MRI-based classification of brain neoplasms11: Classification of brain tumor type and grade using MRI texture and shape12: Brain Tumor Classification Using Efficient Deep Features of MRI ScansSection III: Postoperative Analysis of Brain Tumors 13: Cognitive function of patients with brain tumor in pre-and postoperative stage14: Postoperative complications after craniotomy for brain tumor surgery15: Patient-specific semi-supervised learning for postoperative brain tumor segmentation16: A machine learning approach to predict early outcomes after pituitary adenoma surgery17: Performance evaluation of machine learning algorithms in post-operative life expectancy in the brain cancer patients18: Machine learning algorithm identifies patients at high risk for early complications after intracranial tumor surgery19: Rapid intraoperative diagnosis of pediatric brain tumors using stimulated Raman histology
- No. of pages: 300
- Language: English
- Edition: 1
- Published: January 1, 2029
- Imprint: Academic Press
- Paperback ISBN: 9780323992954
- eBook ISBN: 9780323992961
AS
Ayman S. El-Baz
Dr. El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master’s degrees in Electrical Engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents).
Affiliations and expertise
University of Louisville, USAJS
Jasjit Suri
Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General’s Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.
Affiliations and expertise
Chairman, AtheroPoint LLC, USA