Deep Learning Assessment of Neurological Imaging

Deep Learning Assessment of Neurological Imaging provides an introduction to deep learning structures and pre-processing methods for detecting MRI anomalies. The book also provides a comprehensive accounting of deep learning research on MRI images for Alzheimer's disease, Parkinson's disease, and schizophrenia, and includes a discussion on current research issues and future objectives. This book is a valuable resource to guide new entrants in the field, helping them make a meaningful impact in their development efforts. The book concludes with a brief overview of problems and potential future advancements in the field.

• Introduces Deep Learning structures and pre-processing methods for detecting MRI anomalies
• Discusses current research issues and future research objectives
• Compares a new method using open-access benchmark datasets using Deep Learning on MRI images to detect and classify Alzheimer's Disease, Parkinson's Disease, and Schizophrenia

Tripti Goel is an accomplished professional with a strong academic background and a diverse research portfolio. She earned her Bachelor of Engineering (Hons) from Maharishi Dayanand University in 2004, followed by an MTech in 2008 from Chhotu Ram State College of Engineering and a Ph.D. in 2017 from BPS Mahila Vishwavidyalaya, both in Haryana.

Her career journey includes roles as a lecturer at Bhagwan Mahaveer Institute of Engineering and Technology and Guru Premsukh Memorial College of Engineering in Haryana. Later, she joined NIT Delhi as an Assistant Professor in July 2015 before becoming a Scientist at the National Brain Research Center, Gurugram, in February 2018. Subsequently, she joined NIT Silchar as an Assistant Professor in June 2018.

Dr. Goel's research contributions span deep learning, artificial intelligence, medical imaging, pattern recognition, optimization, and feature extraction. Over the last decade, she has authored 26 conference proceedings, 42 journal publications, 2 patents, and 6 book chapters. Her notable work involves biomarker identification for Alzheimer's diagnosis using MRI and fMRI images. Currently, as the Principal Investigator of a project funded by the Government of India Science & Engineering Research Board (SERB)- Core Research Grant, she is focused on predicting biomarkers for Alzheimer's diagnosis using MRI, susceptibility-weighted imaging (SWI), and FDG-PET images. Dr. Goel's multidisciplinary expertise showcases her significant contributions to the fields of medical imaging and neuroscientific research.

M. Tanveer (Senior Member, IEEE) is a Professor and Ramanujan Fellow at the Department of Mathematics, Indian Institute of Technology Indore. Previously, he was a Postdoctoral Research Fellow at the Rolls-Royce@NTU Corporate Lab, Nanyang Technological University, Singapore. His research interests encompass support vector machines, optimization, machine learning, deep learning, and applications to Alzheimer's disease and dementia. Tanveer has published over 175 refereed journal papers with more than 11700 citations and an h-index of 53+ (Google Scholar, February 2026). He was recently recognized as one of the world’s top 2% scientists in a Stanford University study. He has served on review boards for over 100 scientific journals and participated in various national and international scientific committees. He is the recipient of the 2024 INNS Aharon Katzir Young Investigator Award, 2024 INSA Distinguished Lecture Award, 2023 and 2025 IIT Indore Excellence in Research Award, 2023 INSA Associate Fellow, 2022 Asia Pacific Neural Network Society Young Researcher Award, and the 29th ICONIP 2022 and IJCNN 2024 Best Research Paper Awards. He has held editorial roles for several journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, Pattern Recognition, Neural Networks, and Neurocomputing. Additionally, he has served as Guest Editor for special issues of various journals and co-edited a book on machine intelligence and signal analysis. Tanveer has organized numerous international and national conferences and symposiums as General Chair or Organizing Chair and has delivered keynote talks at various events. He is/was the Principal Investigator (PI) or Co-PI of 14 major research projects funded by the Government of India. He is also an elected member of the Board of Governors of the Asian Pacific Neural Network Society (APNNS) and was appointed the prestigious IEEE CIS Distinguished Lecturer for 2024 -2026. Homepage: https://mtanveer.profiles.iiti.ac.in

Amir Hussain is a highly accomplished academic and researcher with a distinguished career in artificial intelligence (AI) and data science. He earned his B.Eng and Ph.D. degrees from the University of Strathclyde, Glasgow, U.K., in 1992 and 1997, respectively. His academic journey includes positions at various prestigious institutions, culminating in his role as a Professor at Edinburgh Napier University (ENU) since 2018.

At ENU, he serves as the founding Director of the Centre for AI and Robotics (CAIR) and leads the Data Science and Cyber Analytics (DSCA) Research Group, managing over 20 academics and research staff. Additionally, he is the founding Head of the Cognitive Big Data Analytics (CogBiD) Research Lab and co-Lead of the Centre for Cardio-Vascular Health.

Amir Hussain is actively engaged in leadership roles, serving as the Chair of the IEEE UK and Ireland Industry Applications Society Chapter. His advisory roles extend to national and global industries, as well as international government organizations. Notably, he played a crucial role as an international advisor for the Kuwait Government’s Institute for Scientific Research, contributing to the development of the country's National AI Strategy and the National AI Centre of Excellence.

With a prolific publication record, Hussain has authored three international patents, over 500 publications, including international journal papers, books, monographs, and book chapters. His innovative work on functionally expanded neural networks (FENN methodology) resulted in a key patent acquisition by a U.S. company.

As a Principal Investigator, he has led major cross-disciplinary research projects with grants totaling over GBP 5 Million. Hussain has supervised over 35 Ph.D. students, including notable graduates such as Prof Erik Cambria and Dr Soujanya Poria, both recognized for their contributions to AI research. His impactful research, leadership, and advisory roles underscore Amir Hussain's significant contributions to the fields of AI and data science.

Dr. Hussain is a medical professional with a diverse academic and research background. He earned his BSc (Hons) in Medicine from the University of St Andrews, UK, and MBChB Medicine from the University of Edinburgh in 2017 and 2020, respectively. Currently serving as an Academic Core Psychiatry Trainee at Inverclyde Royal Hospital, he is also an Honorary Clinical Lecturer at the University of Glasgow and an Executive Committee Member of the Royal College of Psychiatrists Digital Psychiatry Special Interest Group.

Dr. Hussain actively contributes to medical education as an Associate Faculty Member of the Clinical Educator Programme and undertakes teaching and research activities at Edinburgh Medical School. His involvement includes roles as an SSC1 Tutor, Early Year Guide, and founding Co-ordinator of the FY Clinical Skills Tutor Scheme. Additionally, he participates in medical education research projects and sits on the steering group of the Undergraduate Certificate in Medical Education.

Recognized for his academic achievements, Dr. Hussain was a 2020-22 Foundation Fellow at the Royal College of Psychiatrists, receiving CPD funding. He has held appointments as a Visiting Researcher at institutions such as Boston University School of Public Health, Nanyang Technological University, and the University of Oxford.

Dr. Hussain's research interests center around leveraging artificial intelligence and data science in various healthcare applications, including primary care, public health, neuroscience, psychiatry, ophthalmology, and medical education. He has presented his work at national and international conferences and published in leading academic journals. His commitment extends to leadership roles as the founding President of the Edinburgh Coexistence Initiative, a Board Member for the Edinburgh Interfaith Association, and the Student Activities Liaison Officer for the IEEE UK and Ireland Industry Applications Chapter.