Diagnostic Imaging Systems
- 1st Edition - July 1, 2026
- Latest edition
- Editors: Ayman S. El-Baz, Jasjit S. Suri
- Language: English
Neural engineering is an emerging and fast-moving interdisciplinary research area that combines engineering with (a) electronic and photonic technologies, (b) computer science, (c… Read more
Neural engineering is an emerging and fast-moving interdisciplinary research area that combines engineering with (a) electronic and photonic technologies, (b) computer science, (c) physics, (d) chemistry, (e) mathematics, and (f) cellular, molecular, cognitive, and behavioral neuroscience. This helps us understand the organizational principles and underlying mechanisms of the biology of neural systems and to further to study the behavioral dynamics and complexity of neural systems in nature.
The field of neural engineering deals with many aspects of basic and clinical problems associated with neural dysfunction, including (i) the representation of sensory and motor information, (ii) electrical stimulation of the neuromuscular system to control muscle activation and movement, (iii) the analysis and visualization of complex neural systems at multiscale from the single cell to system levels to understand the underlying mechanisms, (iv) development of novel electronic and photonic devices and techniques for experimental probing, the neural simulation studies, (v) the design and development of human–machine interface systems and artificial vision sensors, and (vi) neural prosthesis to restore and enhance the impaired sensory and motor systems and functions.
To highlight this emerging discipline, Dr. Ayman El-Baz and Dr. Jasjit Suri have developed Advances in Neural Engineering, covering the broad spectrum of neural engineering subfields and applications. This Series includes 7 volumes in the following order: Volume 1: Signal Processing Strategies, Volume 2: Brain-Computer Interfaces, Volume 3: Diagnostic Imaging Systems, Volume 4: Brain Pathologies and Disorders, Volume 5: Computing and Data Technologies, Volume 6: Advanced Brain Imaging Techniques and Volume 7: Neural Science Ethics.
Volume 3 provides a comprehensive review of diagnostic imaging systems and technologies, including brain tumor characterization and classification techniques, tumor segmentation using AI and deep neural networks, dynamic brain imaging analysis, and functional brain imaging. The authors discuss existing challenges in the domain of diagnostic imaging systems and suggest possible research directions.
- Presents Neural Engineering techniques applied to diagnostic imaging systems, brain tumor characterization, brain tumor classification, tumor segmentation, dynamic brain imaging, and functional brain imaging.
- Covers neural imaging data analysis, including brain tumor classification with Deep Learning, segmentation using MRI with Deep Neural Networks, and Machine Learning algorithms for identifying risks of complications.
- Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of signal processing.
Biomedical Engineers and researchers in Neural Engineering, medical imaging, and neural networks. Other interested audiences will be comprised of radiologists, neurologists, neurosurgeons, computer scientists, AI researchers, and designers of Machine Learning applications. Another audience includes those interested in signal processing of the brain and classifying brain signals. Clinicians and researchers interested in neurological diseases and disorders, including their diagnosis and treatment. Tumor imaging oncologists will also be a secondary audience
1. Artificial Intelligence in Diagnostic Imaging for Modern Medicine
2. Yolo-Based Detection and Segmentation: Advancing Real-Time AI in Medical Imaging
3. Breast Cancer Prediction using Image Processing on Big Data: An Empirical Study for Sustainable Health in 21st Century Lifestyle
4. Segmentation and Classification Techniques using AI and their State-of-the-art for the Human Brain Magnetic Resonance Images (MRI)
5. Efficient Artificial Intelligence Techniques for Brain Tumor Classification: Comprehensive Review and Analysis
6. A Novel Cell Nuclei Semantic Segmentation Network for Childhood Medulloblastoma Histopathological Images
7. Exploring AI Innovations for Enhanced Brain Tumor Imaging and Diagnosis
8. Radioengineering Approach for Brain Activity Restoration after Trauma
9. Comparative Analysis of Fast Fuzzy C-Means and Kernel Fuzzy C-Means Algorithms for 3D Brain Tumor Segmentation on the BraTS MICCAI 2021 Dataset
10. SwinUNet Plus: An Adaptive Hierarchical Attention Network for Endoscopic Image Segmentation
11. A YOLO-Based Model for Brain Tumor Detection in Magnetic Resonance Imaging Scans
12. ECG Classification Based CNN Neural Network Models for Arrhythmia Detection
13. Classification of Brain Tumors using AI and Radiomics Techniques
2. Yolo-Based Detection and Segmentation: Advancing Real-Time AI in Medical Imaging
3. Breast Cancer Prediction using Image Processing on Big Data: An Empirical Study for Sustainable Health in 21st Century Lifestyle
4. Segmentation and Classification Techniques using AI and their State-of-the-art for the Human Brain Magnetic Resonance Images (MRI)
5. Efficient Artificial Intelligence Techniques for Brain Tumor Classification: Comprehensive Review and Analysis
6. A Novel Cell Nuclei Semantic Segmentation Network for Childhood Medulloblastoma Histopathological Images
7. Exploring AI Innovations for Enhanced Brain Tumor Imaging and Diagnosis
8. Radioengineering Approach for Brain Activity Restoration after Trauma
9. Comparative Analysis of Fast Fuzzy C-Means and Kernel Fuzzy C-Means Algorithms for 3D Brain Tumor Segmentation on the BraTS MICCAI 2021 Dataset
10. SwinUNet Plus: An Adaptive Hierarchical Attention Network for Endoscopic Image Segmentation
11. A YOLO-Based Model for Brain Tumor Detection in Magnetic Resonance Imaging Scans
12. ECG Classification Based CNN Neural Network Models for Arrhythmia Detection
13. Classification of Brain Tumors using AI and Radiomics Techniques
- Edition: 1
- Latest edition
- Published: July 1, 2026
- Language: English
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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, USA