State of the Art in Neural Networks and Their Applications

Volume 2

1st Edition - November 29, 2022
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

Purchase options

Institutional subscription on ScienceDirect

Request a sales quote

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 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.

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

Jasjit S. 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

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, USA

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health