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Full Description
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.
Contents
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
