State of the Art in Neural Networks and Their Applications
Volume 2
- 1st Edition - November 29, 2022
- Imprint: Academic Press
- Editors: Jasjit S. Suri, Ayman S. El-Baz
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 9 8 7 2 - 8
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 9 9 1 2 - 1
State of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of… Read more
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Request a sales quoteState of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. The book provides over views and case studies of advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing, and suitable data analytics useful for clinical diagnosis and research applications. The application of neural network, artificial intelligence and machine learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases.
State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume One: Neural Networks in Oncology Imaging covers lung cancer, prostate cancer, and bladder cancer. Volume Two: Neural Networks in Brain Disorders and Other Diseases covers autism spectrum disorder, Alzheimer’s disease, attention deficit hyperactivity disorder, hypertension, and other diseases. Written by experienced engineers in the field, these two volumes will help engineers, computer scientists, researchers, and clinicians understand the technology and applications of artificial neural networks.
- Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of oncology imaging technologies
- Provides in-depth technical coverage of computer-aided diagnosis (CAD), including coverage of computer-aided classification, unified deep learning frameworks, 3D MRI, PET/CT, and more
- Covers deep learning cancer identification from histopathological images, medical image analysis, detection, segmentation and classification via AI
Biomedical engineers and researchers in neural engineering, medical imaging, and neural networks and students, researchers, and clinicians in oncology and related fields
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- About the editors
- Acknowledgments
- Chapter 1. Microscopy Cancer Cell Imaging in B-lineage Acute Lymphoblastic Leukemia
- Abstract
- 1.1 Introduction
- 1.2 Building a computer-assisted solution
- 1.3 Data preparation
- 1.4 Normalization of color stain to correct for abnormalities during the staining process
- 1.5 Segmentation of cells of interest (in B-lineage ALL cancer)
- 1.6 Classification of cancer and healthy cells
- 1.7 Conclusions
- References
- Chapter 2. Computational imaging applications in brain and breast cancer
- Abstract
- 2.1 Introduction
- 2.2 Building upon current clinical standards
- 2.3 Deep learning applications in brain cancer
- 2.4 Deep learning applications in breast cancer
- 2.5 Conclusion
- Acknowledgments
- References
- Chapter 3. Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases
- Abstract
- 3.1 Introduction and motivation for the early diagnosis of melanoma
- 3.2 Artificial intelligence and computer vision in melanoma diagnosis
- 3.3 Medical diagnostic procedures for screening of skin diseases
- 3.4 State-of-the-art survey on skin mole segmentation methods
- 3.5 Improved local and global patterns detection algorithms by deep learning algorithms
- 3.6 Early classification of skin melanomas in dermoscopy
- 3.7 Conclusions
- 3.8 How to speed up the classification process with field-programmable gate arrays?
- 3.9 Challenges and future directions
- 3.10 Teledermatology
- References
- Chapter 4. An accurate deep learning-based computer-aided diagnosis system for early diagnosis of prostate cancer
- Abstract
- 4.1 Introduction
- 4.2 Methods
- 4.3 Experimental results
- 4.4 Conclusion
- References
- Chapter 5. Adaptive graph convolutional neural network and its biomedical applications
- Abstract
- 5.1 Introduction
- 5.2 Related work
- 5.3 Method
- 5.4 Experiment
- 5.5 Conclusion
- References
- Further reading
- Chapter 6. Deep slice interpolation via marginal super-resolution, fusion, and refinement
- Abstract
- 6.1 Introduction
- 6.2 Related work
- 6.3 Problem formulation and baseline convolutional neural networks approaches
- 6.4 The proposed algorithm
- 6.5 Experiments
- 6.6 Conclusion
- References
- Chapter 7. Explainable deep learning approach to predict chemotherapy effect on breast tumor’s MRI
- Abstract
- 7.1 Introduction
- 7.2 Materials and developed methods
- 7.3 Results
- 7.4 Discussion
- 7.5 Conclusion
- Aknowledgments
- References
- Chapter 8. Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features
- Abstract
- 8.1 Introduction
- 8.2 Related work on interpretable artificial intelligence
- 8.3 Methods
- 8.4 Experiments and results
- 8.5 Discussion of the results
- 8.6 Conclusions
- Acknowledgments
- References
- Chapter 9. Computational lung sound classification: a review
- Abstract
- 9.1 Introduction
- 9.2 Data processing
- 9.3 Data modeling
- 9.4 Recent public lung sound datasets
- 9.5 Conclusion
- References
- Chapter 10. Clinical applications of machine learning in heart failure
- Abstract
- 10.1 Introduction
- 10.2 Diagnosis
- 10.3 Management
- 10.4 Prevention
- 10.5 Conclusion
- References
- Chapter 11. Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey
- Abstract
- 11.1 Introduction
- 11.2 Basic background
- 11.3 Steps of artificial intelligence-based diagnostic systems
- 11.4 Texture analysis
- 11.5 Clinical applications of artificial intelligence and radiomics
- 11.6 Merits and limitations
- 11.7 Future directions
- 11.8 Conclusion
- References
- Chapter 12. A review of texture-centric diagnostic models for thyroid cancer using convolutional neural networks and visualized texture patterns
- Abstract
- 12.1 Introduction
- 12.2 Materials and collection protocols
- 12.3 Statistical analysis
- 12.4 2D texture model
- 12.5 3D texture model
- 12.6 Texture analysis
- 12.7 Results
- 12.8 Discussion
- 12.9 Conclusion
- References
- Index
- Edition: 1
- Published: November 29, 2022
- No. of pages (Paperback): 326
- No. of pages (eBook): 326
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128198728
- eBook ISBN: 9780128199121
JS
Jasjit S. Suri
Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.
Affiliations and expertise
Chairman, AtheroPoint LLC, USAAS
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, USARead State of the Art in Neural Networks and Their Applications on ScienceDirect