Overview
- Offers an in-depth review of existing approaches used in the diagnosis of neurodegenerative diseases
- Presents key concepts for using AI in the diagnosis of neurological disorders with machine learning methods
- Addresses signal and medical image analysis with the integration of genetic data for processing neurological big data
Part of the book series: Cognitive Technologies (COGTECH)
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About this book
This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders.
This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features:
● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection.
● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders.
● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.
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Keywords
Table of contents (15 chapters)
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Conclusions and Future Perspectives for Automated Neurodegenerative Disorders Diagnosis
Editors and Affiliations
About the editors
Deepak Kumar Jain works as Assistant Professor at Institute of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China. He received the Bachelor of Engineering degree from Rajiv Gandhi Proudyogiki Vishwavidyalaya, India, in 2010, the Master of Technology degree from the Jaypee University of Engineering and Technology, India, in 2012 andthe Ph.D. degree from the Institute of Automation, University of Chinese Academy of Sciences, Beijing, China. He was an awardee of CAS-TWAS Presidential Fellowship from 2014 to 2018. He was invited as “Foreign Experts” by Shandong Taian Administration of Foreign Expert Affairs. He has presented several papers in peer-reviewed conferences and has published numerous studies in science cited journals. His research interests include deep learning, machine learning, pattern recognition and computer vision.
Yanhui Guo received his Ph.D. degree from the Department of Computer Science, Utah State University, USA. He was Research Fellow in the Department of Radiology at the University of Michigan and Assistant Professor at St. Thomas University. Dr. Guo is currently Associate Professor in the Department of Computer Science at the University of Illinois Springfield. Dr. Guo’s research area includes computer vision, machine learning, data analytics, neutrosophic set, computer-aided detection/diagnosis and computer-assisted surgery. He has published 3 books, more than 110 journal papers and 40 conference papers, completed more than 10 grant-funded research projects, has 2 patents and worked as Associate Editor of different international journals and Reviewer for top journals and conferences. Dr. Guo successfully applied neutrosophic set into image processing in 2008 and has published many research works in this area. Dr. Guo was Co-founder and Chief Scientist of MedSights Tech Inc., a high technology company focusing on a computer-assisted surgery system. Dr. Guo was awarded a University Scholar in 2019, the university system’s highest faculty honor, recognizing outstanding teaching and scholarship.
Amira S. Ashour is Assistant Professor and Head of Department at Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University, Egypt, since 2016. She is Head of the MICSCAS Research Laboratory (Medical Image Computing Systems and Computer-Assisted Surgery Laboratory), Faculty of Engineering, Tanta University, Egypt. She is ICT Manager of Huawei Academy, Tanta University, Egypt, during 2019 and 2020. She obtained her M.Sc. degree in electrical engineering (Enhancement of Electromagnetic Non-Destructive Evaluation Performance using Advanced Signal Processing Techniques) and Ph.D. in smart antenna from the Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt. Her research interests include Biomedical Engineering, Medical Devices, Medical Image and Signal Processing, Medical Imaging, Ablation Therapy, Machine Learning, Optimization, Smart Antenna, Target Tracking and Direction of Arrival Estimation. She is Co-editor of Advances in Ubiquitous Sensing Applications for Healthcare, book series, Elsevier. She is Editorial Board Member of several reputable journals. She published 120 journal papers and conference proceedings and edited/authored about 20 books (Elsevier/Springer).
Atef Zaguia received the bachelor’s degree in computer engineering from the University of Ottawa and the M.S. and Ph.D. degrees in computer science from the École de Téchnologie Supérieure (E.T.S.), University of Quebec, Montreal, Canada. For one year, he held Postdoctoral position at E.T.S., University of Quebec. He was working on developing application for newborn cry-based diagnosis system with the integration of interaction context, supported by the Bill and Melinda Gates Foundation. He is currently Associate Professor at the College of Computers and Information Technology, Taif University, Saudi Arabia. He has published papers in national and international conferences and journals. His research interests include multimodal systems, pervasive and ubiquitous computing, IoT, AI and context-aware systems. He was Program Committee Member of the Tenth International Conference on Mobile UbiquitousComputing, Systems, Services and Technologies (UBICOMM 2016), Venice, Italy.
Bibliographic Information
Book Title: Data Analysis for Neurodegenerative Disorders
Editors: Deepika Koundal, Deepak Kumar Jain, Yanhui Guo, Amira S. Ashour, Atef Zaguia
Series Title: Cognitive Technologies
DOI: https://doi.org/10.1007/978-981-99-2154-6
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Hardcover ISBN: 978-981-99-2153-9Published: 01 June 2023
Softcover ISBN: 978-981-99-2156-0Published: 02 June 2024
eBook ISBN: 978-981-99-2154-6Published: 31 May 2023
Series ISSN: 1611-2482
Series E-ISSN: 2197-6635
Edition Number: 1
Number of Pages: VIII, 267
Number of Illustrations: 11 b/w illustrations, 68 illustrations in colour
Topics: Health Informatics, Computer Imaging, Vision, Pattern Recognition and Graphics, Signal, Image and Speech Processing, Machine Learning, Data Structures and Information Theory, Artificial Intelligence