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Computational Intelligence
Algorithms for the Diagnosis of
Neurological Disorders
This book delves into the transformative potential of articial intelligence (AI) and
machine learning (ML) as game-changers in diagnosing and managing 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 compu-
tational intelligence empowers healthcare professionals in the ght 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 efcient 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 ML for pattern recognition, enabling
the identication 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.
Edge AI in Future Computing
Series Editors:
Arun Kumar Sangaiah, SCOPE, VIT University, Tamil Nadu
Mamta Mittal, G. B. Pant Government Engineering College, Okhla, New Delhi
AI-Driven IoT Systems for Industry 4.0
Deepa Jose, Paul Sanchita, Sachi Nandan Mohanty, Preethi Nanjundan
Big Data and Edge Intelligence for Enhanced Cyber Defense: Principles and
Research
Chhabi Rani Panigrahi, Victor Hugo C. de Albuquerque, Akash Kumar Bhoi,
Hareesha K. S.
Soft Computing Techniques in Engineering, Health, Mathematical and Social
Sciences
Pradip Debnath and S. A. Mohiuddine
Machine Learning for Edge Computing: Frameworks, Patterns and Best Practices
Amitoj Singh, Vinay Kukreja, Taghi Javdani Gandomani
Internet of Things: Frameworks for Enabling and Emerging Technologies
Bharat Bhushan, Sudhir Kumar Sharma, Bhuvan Unhelkar, Muhammad Fazal Ijaz,
Lamia Karim
Soft Computing: Engineering Applications
Pradip Debnath and Binod Chandra Tripathy
Soft Computing: Recent Advances and Applications in Engineering and
Mathematical Sciences
Pradip Debnath, Oscar Castillo, Poom Kumam
Computational Statistical Methodologies and Modeling for Articial Intelligence
Priyanka Harjule, Azizur Rahman, Basant Agarwal, and Vinita Tiwari
Industry 5.0 for Smart Healthcare Technologies: Utilizing Articial Intelligence,
Internet of Medical Things and Blockchain
Edited by Sherin Zafar, S. N. Kumar, A. Ahilan, and Gulsun Kurubacak Cakir
Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders
Edited by S.N. Kumar, Sherin Zafar, and Sameena Naaz
For more information about this series, please visit: https://www.routledge.com/
Edge-AI-in-Future-Computing/book-series/EAIFC
Computational
Intelligence Algorithms for
the Diagnosis of
Neurological Disorders
Edited by S. N. Kumar, Sherin Zafar,
and Sameena Naaz
Designed cover image: Shutterstock ©
First edition published 2026
by CRC Press
2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
CRC Press is an imprint of Taylor & Francis Group, LLC
© 2026 selection and editorial matter, S. N. Kumar, Sherin Zafar, and Sameena Naaz; individual chapters,
the contributors
Reasonable efforts have been made to publish reliable data and information, but the author and publisher
cannot assume responsibility for the validity of all materials or the consequences of their use. The authors
and publishers have attempted to trace the copyright holders of all material reproduced in this publication and
apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright
material has not been acknowledged please write and let us know so we may rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter
invented, including photocopying, microlming, and recording, or in any information storage or retrieval
system, without written permission from the publishers.
For permission to photocopy or use material electronically from this work, access www.copyright.com
or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923,
978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@tandf.
co.uk
Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used
only for identication and explanation without intent to infringe.
ISBN: 978-1-032-85890-6 (hbk)
ISBN: 978-1-032-85891-3 (pbk)
ISBN: 978-1-003-52034-4 (ebk)
DOI: 10.1201/9781003520344
Typeset in Times
by KnowledgeWorks Global Ltd.
Contents
Preface.......................................................................................................................ix
About the Editors......................................................................................................xi
List of Contributors................................................................................................ xiii
PART I Introduction and Challenges
Chapter 1 Introduction to Neurological Disorders................................................3
T. Manonmani, Mohit Malik, and P. Abinaya
Chapter 2 Navigating the Complexities of the Brain: Challenges
and Opportunities in Computational Neurology................................ 18
Ginni Arora, Alvaro Rocha, and Syamsundar Patta
Chapter 3 Challenges and Opportunities in Computational
Neurology........................................................................................... 31
S. Vijayanand and C. Priya
Chapter 4 Ethical Issues in Neurodisorder Diagnosis ........................................45
Runa Hussain, Safdar Tanweer, Sameena Naaz,
and Sherin Zafar
Chapter 5 Ethical Issues in Neurodisorder Diagnosis: Computational
Intelligence toward Compassionate Psychiatric Treatment................54
Bhupinder Singh, Rishabha Malviya,
and Christian Kaunert
PART II Neuroimaging and Diagnostic Techniques
Chapter 6 Improving Magnetic Resonance Imaging (MRI)
for Better Understanding of Neurological Disorders .........................69
Mohd Abdullah Siddiqui, Sohrab A. Khan, Charu Chhabra,
Sahar Zaidi, and Habiba Sundus
v
vi Contents
Chapter 7 Advancements in Neuroimaging Techniques in Encephalopathy........ 80
Firdaus Jawed, Rabia Aziz, Sohrab Ahmad Khan, Sumbul
Ansari, and Shahnawaz Anwer
Chapter 8 Targeted Drug Delivery for Neurological Disorders.......................... 90
Bhupen Kalita
Chapter 9 Intelligent Deep Learning Algorithms for Autism Spectrum
Disorder Diagnosis........................................................................... 109
V. Thamilarasi, R. Roselin, P. Pushpa, M. Kannan,
and B. P. Sreejith Vignesh
Chapter 10 Advanced Neuroimaging with Generative Adversarial
Networks................................................................................... 124
Basil Hana, Mohammad Ubaidullah Bokhari, and Imran Khan
Chapter 11 Machine Learning Strategy with Decision Trees for
Parkinson’s Detection by Analyzing the Energy of the
Acoustic Data ............................................................................................148
P. Arun, Enrico M. Staderini, S. Madhukumar, P. Careena,
P. V. Sarath, and P. R. Sreesh
Chapter 12 Adaptive Convolution Neural Network-Based Brain Tumor
Detection from MR Images.............................................................. 161
C. Prajitha, K. Thamaraiselvi, S. Rinesh, K. P. Sridhar,
and K. M. Abubeker
Chapter 13 STN-DRN: Integrating Spatial Transformer Network
with Deep Residual Network for Multiclass Classication
of Alzheimer’s Disease .........................................................................174
Prabu Selvam, S. Sudharson, and P. N. Senthil Prakash
PART III Machine Learning and AI Applications in
Neurological Disorders
Chapter 14 Evaluation of Supervised Learning Algorithms in Detection
of Neurodisorders: A Focus on Parkinson’s Disease ....................... 193
Chitigala Mouleeshwari, C. Kishor Kumar Reddy,
D. Manoj Kumar Reddy, and Srinath Doss
Chapter 15 Comparative Analysis of Supervised and Unsupervised
Learning Algorithms in the Detection of Alzheimer’s
Disease ............................................................................................. 219
V. A. Binson, Starlet Ben Alex, and Rangith Kuriakose
Chapter 16 Deep Learning Techniques in Neurological Disorder
Detection .......................................................................................... 239
Manisha Nagar, Shikha Singh, Sanjay Singh,
and Ruchi Jain
Chapter 17 From Data to Diagnosis: Supervised Learning’s Impact on
Neurodisorder Detection, with a Focus on Autism Spectrum
Disorder............................................................................................ 257
S. Srividhya and S. R. Lavanya
Chapter 18 Parkinson’s Disease Detection from Drawing Images Using
Deep Pretrained Models................................................................... 269
Sourabh Shastri, Sachin Kumar, and Vibhakar Mansotra
vii Contents
Chapter 19 Optimizing Digital Healthcare for Alzheimer’s Disease: A
Deep Federated Learning Convolutional Neural Network
Scheme (DFLCNNS) .......................................................................290
Swathi Sambangi, T. Kusuma, D. Srinivasa Rao, G.
Lakshmeeswari, and Rakhee
Chapter 20 Articial Intelligence: A Game-Changer in Parkinson’s
Disease Neurorehabilitation............................................................. 311
Nabeela Rehman, Arshya Anwar, and Sahar Zaidi
Chapter 21 Targeting Upper-Limb Sensory Gaps: New Rehab
Insights for Chronic Neck Pain ........................................................ 322
Sahar Zaidi, Sohrab Ahmad Khan, Charu Chhabra,
Habiba Sundus, and Irshad Ahmad
Index...................................................................................................................... 333
Preface
Neurological disorders represent a signicant and growing challenge in modern medicine, affecting millions of individuals worldwide. With advancements in computational intelligence (CI), articial intelligence (AI), and machine learning (ML), we
are witnessing a paradigm shift in how these disorders are diagnosed, monitored, and
managed. The fusion of cutting-edge computational methods with neuroscience has
the potential to revolutionize early detection, enhance treatment efcacy, and provide
deeper insights into the complexities of the human brain. This book, Computational
Intelligence Algorithms for the Diagnosis of Neurological Disorders, aims to present a
comprehensive overview of the latest research and developments in this interdisciplinary eld. It brings together leading experts from across the globe to explore the role of
computational techniques in addressing neurological conditions such as Parkinson’s disease, Alzheimer’s disease, autism spectrum disorder, and brain tumors, among others.
Structured into three major sections, this book begins with an introduction
to neurological disorders and the challenges associated with computational neurology. Ethical considerations in neurodisorder diagnosis and treatment are also
discussed, emphasizing the need for the compassionate and responsible application of articial intelligence. The second section delves into neuroimaging and
diagnostic techniques, highlighting advancements in magnetic resonance imaging
(MRI), deep learning applications, and targeted drug delivery. These technologies
have signicantly enhanced our ability to detect, classify, and analyze neurological disorders with higher precision and accuracy. The nal section focuses on the
application of ML and AI in neurological disorder diagnosis. From supervised
learning models to deep learning and federated learning approaches, this section
demonstrates how AI-driven solutions are shaping the future of neurorehabilitation and patient care.
The objective of this book is to serve as a valuable resource for researchers, medical
professionals, computer scientists, and students interested in the intersection of computational intelligence and neurology. By fostering a deeper understanding of AI’s role
in neuroscience, we hope to contribute to more effective diagnostic methodologies and
ultimately improve patient outcomes. We extend our sincere gratitude to the authors,
researchers, and professionals who have contributed to this book. Their dedication and
expertise have made it possible to present a comprehensive and insightful compilation
of knowledge. We also appreciate the support of the institutions and organizations that
have encouraged this endeavor. We hope that this book inspires further research and
innovation in computational intelligence for neurological disorder diagnosis, paving
the way for breakthroughs that will transform the future of medical science.
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