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
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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
- 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
- No. of pages: 326
- Language: English
- Edition: 1
- Published: November 29, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128198728
- eBook ISBN: 9780128199121
JS
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.
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