Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Häftad, Engelska, 2018

AvNilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi,UK) Dey, Nilanjan (Associate Professor, Department of Computer Science and Engineering, Techno International New Town, Kolkata, India; Visiting Fellow , University of Reading,India) Borra, Surekha (Department of Electronics and Communication Engineering, K.S. Institute of Technology, Bangalore,Egypt) Ashour, Amira S. (Assistant Professor and Head of Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University,USA) Shi, Fuqian, PhD. (Rutgers Cancer Institute of New Jersey

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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented.

The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers.

Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging
Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining
Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Produktinformation

Utgivningsdatum2018-12-05
Mått191 x 235 x undefined mm
Vikt730 g
FormatHäftad
SpråkEngelska
Antal sidor345
FörlagElsevier Science
ISBN9780128160862

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Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.Surekha Borra is currently a Professor in the Department of ECE, K. S. Institute of Technology, Bangalore, India. She earned her Doctorate in Image Processing from Jawaharlal Nehru Technological University, Hyderabad, India, in 2015. Her research interests are in the areas of Image and Video Analytics, Machine Learning, Biometrics and Remote Sensing. She has published 1 edited book, 8 book chapters and 22 research papers to her credit in refereed & indexed journals, and conferences at international and national levels. Her international recognition includes her professional memberships & services in refereed organizations, programme committees, editorial & review boards, wherein she has been a guest editor for 2 journals and reviewer for journals published by IEEE, IET, Elsevier, Taylor & Francis, Springer, IGI-Global etc,. She has received Woman Achiever's Award from The Institution of Engineers (India), for her prominent research and innovative contribution (s)., Woman Educator & Scholar Award for her contributions to teaching and scholarly activities, Young Woman Achiever Award for her contribution in Copyright Protection of Images. Amira S. Ashour is an Assistant Professor and Head of Electronics and Electrical Communications Engineering Department, Faculty of Engineering, Tanta University, Egypt. She is a member in the Research and Development Unit, Faculty of Engineering, Tanta University, Egypt. She received the B.Eng. degree in Electrical Engineering from Faculty of Engineering, Tanta University, Egypt in 1997, M.Sc. in Image Processing in 2001 and Ph.D. in Smart Antenna in 2005 from Faculty of Engineering, Tanta University, Egypt. Ashour has been the Vice Chair of Computer Engineering Department, Computers and Information Technology College, Taif University, KSA for one year from 2015. She has been the vice chair of CS department, CIT college, Taif University, KSA for 5 years. Her research interests are Smart antenna, Direction of arrival estimation, Targets tracking, Image processing, Medical imaging, Machine learning, Biomedical Systems, Pattern recognition, Image analysis, Computer vision, Computer-aided detection and diagnosis systems, Optimization, and Neutrosophic theory. She has 15 books and about 150 published journal papers. She is an Editor-in-Chief for the International Journal of Synthetic Emotions (IJSE), IGI Global, US. Fuqian Shi (Senior Member, IEEE) received the Ph.D. degree in engineering from the College of Computer Science and Technology, Zhejiang University. He was a Visiting Associate Professor with the Department of Industrial Engineering and Management System, University of Central Florida, Orlando, FL, USA, from 2012 to 2014. He is currently an Associate Professor with the Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. He serves more than 30 committee board memberships for international conferences. He has published more than 80 journal articles and conference proceedings. His research interests include fuzzy inference system, artificial neuro networks, and biomechanical engineering. He also serves as an Associate Editor for the International Journal of Ambient Computing and Intelligence (IJACI), the International Journal of Rough Sets and Data Analysis (IJRSDA), and a Special Issue Editor of fuzzy engineering and intelligent transportation in Information: An International Interdisciplinary Journal

1. Ontology-based Process for Unstructured Medical Report Mapping2. A Computer-aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images3. A DEFS based System for Differential Diagnosis between Severe Fatty Liver and Cirrhotic Liver using Ultrasound Images4. Infrared Thermography and Soft Computing for Diabetic Foot Assessment5. Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN and SVM Classifiers using HRV Analysis6. Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization using Digitized Screen Film Mammograms7. Optimization of ANN architecture: A review on nature-inspired techniques8. Ensemble Learning Approach to Motor-Imagery EEG Signal Classification9. Medical Images Analysis Based on Multi-Label Classification Methods10. Figure Search in Biomedical Domain: A Survey of Techniques and Challenges11. Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images12. Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective

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