State of the Art in Neural Networks and Their Applications
Volume 1
- 1st Edition - July 21, 2021
- Imprint: Academic Press
- Editors: Ayman S. El-Baz, Jasjit S. Suri
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 9 7 4 0 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 8 4 9 - 5
State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagno… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteState of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. 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 are covered, including relevant case studies. 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 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.
State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.
- Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies
- Provides in-depth technical coverage of computer-aided diagnosis (CAD), with coverage of computer-aided classification, Unified Deep Learning Frameworks, mammography, fundus imaging, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more
- Covers deep learning for several medical conditions including renal, retinal, breast, skin, and dental abnormalities, Medical Image Analysis, as well as 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
- Biographies
- Acknowledgments
- Chapter 1. Computer-aided detection of abnormality in mammography using deep object detectors
- Abstract
- 1.1 Introduction
- 1.2 Literature review
- 1.3 Methodology
- 1.4 Experimental results
- 1.5 Discussions
- 1.6 Conclusion
- References
- Chapter 2. Detection of retinal abnormalities in fundus image using CNN deep learning networks
- Abstract
- 2.1 Introduction
- 2.2 Earlier screening and diagnosis of ocular diseases with CNN deep learning networks
- 2.3 Deep learning–based smartphone for detection of retinal abnormalities
- 2.4 Discussion
- 2.5 Conclusion
- References
- Chapter 3. A survey of deep learning-based methods for cryo-electron tomography data analysis
- Abstract
- 3.1 Introduction
- 3.2 Deep learning-based methods
- 3.3 Conclusion
- References
- Chapter 4. Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural network features
- Abstract
- 4.1 Introduction
- 4.2 Related work
- 4.3 Fédération Dentaire Internationale tooth numbering system
- 4.4 The method
- 4.5 Experimental analysis
- 4.6 Discussion and conclusions
- References
- Chapter 5. Accurate identification of renal transplant rejection: convolutional neural networks and diffusion MRI
- Abstract
- 5.1 Introduction
- 5.2 Methods
- 5.3 Experimental results
- 5.4 Conclusion
- Acknowledgments
- References
- Chapter 6. Applications of the ESPNet architecture in medical imaging
- Abstract
- 6.1 Introduction
- 6.2 Background
- 6.3 The ESPNet architecture
- 6.4 Experimental results
- 6.5 Conclusion
- Acknowledgment
- References
- Chapter 7. Achievements of neural network in skin lesions classification
- Abstract
- 7.1 Introduction
- 7.2 Literature review
- 7.3 Background
- 7.4 Dataset
- 7.5 Methodology
- 7.6 Results and discussion
- 7.7 Conclusion
- References
- Chapter 8. A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images
- Abstract
- 8.1 Introduction
- 8.2 Background
- 8.3 Datasets
- 8.4 Methodology
- 8.5 Conclusion
- References
- Chapter 9. Computer-aided diagnosis of renal masses
- Abstract
- 9.1 Introduction
- 9.2 Segmentation of kidneys
- 9.3 Kidney tumor localization
- 9.4 Differentiation of malignant versus benign renal masses
- 9.5 Future perspectives
- References
- Chapter 10. Early identification of acute rejection for renal allografts: a machine learning approach
- Abstract
- Acknowledgment
- 10.1 Introduction
- 10.2 Methods
- 10.3 Experimental results
- 10.4 Conclusion
- References
- Chapter 11. Deep learning for computer-aided diagnosis in ophthalmology: a review
- Abstract
- 11.1 Introduction
- 11.2 Deep learning: the methods
- 11.3 Limitations of the state-of-the-art
- 11.4 Beyond convolutional neural networks
- 11.5 Conclusion
- References
- Chapter 12. Deep learning for ophthalmology using optical coherence tomography
- Abstract
- 12.1 Introduction
- 12.2 Optical coherence tomography
- 12.3 Retinal biomarkers and diseases
- 12.4 Traditional approaches for ophthalmic diagnosis
- 12.5 Deep learning approaches to optical coherence tomography analysis
- 12.6 Final thoughts
- Acknowledgment
- Conflict of interest
- References
- Further reading
- Chapter 13. Generative adversarial networks in medical imaging
- Abstract
- 13.1 Introduction
- 13.2 Applications in medical imaging
- 13.3 Conclusions
- References
- Chapter 14. Deep learning from small labeled datasets applied to medical image analysis
- Abstract
- 14.1 Introduction
- 14.2 Cross-modality deep learning
- 14.3 Example of cross-domain adaptation-based segmentation: lung tumor segmentation from MRI
- 14.4 Results
- 14.5 Future outlook and discussion
- 14.6 Conclusion
- References
- Index
- Edition: 1
- Published: July 21, 2021
- No. of pages (Paperback): 324
- No. of pages (eBook): 324
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128197400
- eBook ISBN: 9780128218495
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 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, USARead State of the Art in Neural Networks and Their Applications on ScienceDirect