This book delves into the transformative potential of artificial intelligence (AI) and machine learning (ML) as game-changers in diagnosing and managing of neurodisorder conditions. It covers a wide array of methodologies, algorithms, and applications in depth.
This book delves into the transformative potential of artificial intelligence (AI) and machine learning (ML) as game-changers in diagnosing and managing of neurodisorder conditions. It covers a wide array of methodologies, algorithms, and applications in depth.
Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders equips readers with a comprehensive understanding of how computational intelligence empowers healthcare professionals in the fight against neurodisorders. Through practical examples and clear explanations, it explores the diverse applications of these technologies, showcasing their ability to analyze complex medical data, identify subtle patterns, and contribute to the development of more accurate and efficient diagnostic tools. The authors delve into the exciting possibilities of AI-powered algorithms, exploring their ability to analyze various data sources like neuroimaging scans, genetic information, and cognitive assessments. They also examine the realm of machine learning for pattern recognition, enabling the identification of early disease markers and facilitating timely intervention. Finally, the authors also address the critical challenges of data privacy and security, emphasizing the need for robust ethical frameworks to safeguard sensitive patient information.
This book aims to spark a conversation and foster collaboration among researchers, clinicians, and technologists and will assist radiologists and neurologists in making precise diagnoses with enhanced accuracy.
PART A: Introduction and Challenges
Chapter 1 Introduction to
Neurological Disorders
Chapter 2 Navigating the Complexities of the Brain
Challenges and Opportunities in Computational Neurology
Chapter 3 Challenges
and Opportunities in Computational Neurology
Chapter 4 Ethical Issues in
Neurodisorder Diagnosis
Chapter 5 Ethical Issues in Neurodisorder Diagnosis:
Computational Intelligence towards Compassionate Psychiatric Treatment
Part-B: Neuroimaging and Diagnostic Techniques
Chapter 6 Improving Magnetic
Resonance Imaging (MRI) for Better Understanding of Neurological Disorders
Chapter 7 Advancements in Neuroimaging technique in Encephalopathy
Chapter 8
Targeted Drug Delivery for Neurological Disorders
Chapter 9 Intelligent Deep
Learning Algorithms for Autism Spectrum Disorder Diagnosis
Chapter 10
Advanced Neuroimaging with Generative Adversarial Networks
Chapter 11 Machine
Learning Strategy with Decision Trees for Parkinson's Detection by Analyzing
the Energy of the Acoustic Data
Chapter 12 Adaptive Convolution Neural
Network-based Brain Tumor Detection from MR Images
Chapter 13 STN-DRN:
Integrating Spatial Transformer Network with Deep Residual Network for
Multiclass Classification of Alzheimers Disease Part C: Machine Learning &
AI Applications in Neurological Disorders
Chapter 14 Evaluation of Supervised
Learning Algorithms in Detection of Neurodisorders: A Focus on Parkinson's
Disease
Chapter 15 Comparative Analysis of Supervised and Unsupervised
Learning Algorithms in the Detection of Alzheimers disease
Chapter 16 Deep
Learning Techniques in Neurological Disorder Detection
Chapter 17 From Data
to Diagnosis: Supervised Learning's Impact on Neuro-disorder detection, with
a focus on Autism Spectrum Disorder
Chapter 18 Parkinson's Disease Detection
from Drawing Images using Deep Pretrained Models
Chapter 19 Optimizing
Digital Healthcare for Alzheimer's: A Deep Federated Learning Convolutional
Neural Network Scheme (DFLCNNS)
Chapter 20 Artificial Intelligence: A
Game-Changer in Parkinsons Disease Neurorehabilitation
Chapter 21 Targeting
Upper Limb Sensory Gaps: New Rehab Insights for Chronic Neck Pain
S. N. Kumar received his B.E. degree from the Department of Electrical and Electronics Engineering, Sun College of Engineering and Technology, in 2007, his M.E. degree in applied electronics from the Anna University of Technology, Tirunelveli, and his Ph.D. degree from the Sathyabama Institute of Science and Technology in 2019. He is currently an Associate Professor with the Department of Electrical and Electronics Engineering, Amal Jyothi College of Engineering, Kanjirappally, and his research areas include medical image processing and embedded systems.
Sherin Zafar is an Assistant Professor of Computer Science and Engineering at the School of Engineering Sciences and Technology, Jamia Hamdard University, with a decade of successful experience in teaching and research management. She specializes in wireless networks, soft computing, and network security.
Sameena Naaz is a Senior Lecturer at the Department of Computer Science, School of Arts, Humanities and Social Sciences at the University of Roehampton, London, UK, with more than 22 years of experience. She received her M.Tech. degree in Electronics with Specialization in Communication and Information Systems from Aligarh Muslim University in 2000 and completed her Ph.D. from Jamia Hamdard in the field of distributed systems in 2014. Her research interests include distributed systems, cloud computing, big data, machine learning, data mining, and image processing.
