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State of the Art in Neural Networks and Their Applications
Volume 1
1st Edition - July 21, 2021
Editors: Ayman S. El-Baz, Jasjit Suri
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… Read more
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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 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.
- 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
1. Computer Aided Detection of Abnormality in Mammography using Deep Object Detectors
2. Detection of retinal abnormalities in fundus image using CNN Deep Learning Networks
3. A survey of Deep Learning Based Methods for Cryo-electron Tomography Data Analysis
4. Detection, Segmentation and Numbering of Teeth in Dental Panoramic Images with Mask RCNN
5. Accurate Identification of Renal Transplant Rejection: Convolutional Neural Networks and Diffusion MRI
6. Applications of the ESPNet Architecture in Medical Imaging
7. Achievements of Neural Network in Skin Lesions Classification
8. A Computer-aided-diagnosis System for Breast Cancer Molecular Subtypes Prediction in mammographic images
9. Computer-Aided Diagnosis of Renal Masses
10. Early Identification of Acute Rejection for Renal Allografts: A Machine Learning Approach
11. Deep Learning for Computer-Aided Diagnosis in Ophthalmology: A Review
12. Deep Learning for Ophthalmology using Optical Coherence Tomography
13. Generative Adversarial Networks in Medical Imaging
14. Deep Learning from Small Labeled Datasets Applied to Medical Image Analysis
2. Detection of retinal abnormalities in fundus image using CNN Deep Learning Networks
3. A survey of Deep Learning Based Methods for Cryo-electron Tomography Data Analysis
4. Detection, Segmentation and Numbering of Teeth in Dental Panoramic Images with Mask RCNN
5. Accurate Identification of Renal Transplant Rejection: Convolutional Neural Networks and Diffusion MRI
6. Applications of the ESPNet Architecture in Medical Imaging
7. Achievements of Neural Network in Skin Lesions Classification
8. A Computer-aided-diagnosis System for Breast Cancer Molecular Subtypes Prediction in mammographic images
9. Computer-Aided Diagnosis of Renal Masses
10. Early Identification of Acute Rejection for Renal Allografts: A Machine Learning Approach
11. Deep Learning for Computer-Aided Diagnosis in Ophthalmology: A Review
12. Deep Learning for Ophthalmology using Optical Coherence Tomography
13. Generative Adversarial Networks in Medical Imaging
14. Deep Learning from Small Labeled Datasets Applied to Medical Image Analysis
- No. of pages: 324
- Language: English
- Published: July 21, 2021
- Imprint: Academic Press
- 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 LouisvilleJS
Jasjit Suri
Jasjit S. Suri, PhD, MBA, FIEEE, FAMIBE, FAIUM, FSVM, FAPVS is an innovator, visionary, scientist, and an internationally known world leader in Biomedical Engineering and its management. Dr. Suri received the Director General's Gold medal in 1980, is a Fellow of the Institute of Electrical and Electronics Engineers (2021), American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC (2004), American Institute of Ultrasound in Medicine (2019), Asia Pacific Vascular Society (2020) and recipient of Lifetime Achievement Award from Marquis (2018). He is currently Chairman of AtheroPoint, Roseville, CA, USA, dedicated to imaging technologies for cardiovascular and stroke. He has nearly 30,000 citations, has co-authored 60 books, and 50 US/European Patents.
Affiliations and expertise
Chairman, AtheroPoint LLC