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.
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.
1. Ontology-based Process for Unstructured Medical Report
Mapping
2. A Computer-aided Diagnoses System for Detecting Multiple Ocular
Diseases Using Color Retinal Fundus Images
3. A DEFS based System for Differential Diagnosis between Severe
Fatty Liver and Cirrhotic Liver using Ultrasound Images
4. Infrared Thermography and Soft Computing for Diabetic Foot
Assessment
5. Automated Classification of Hypertension and Coronary Artery
Disease Patients by PNN, KNN and SVM Classifiers using HRV
Analysis
6. Optimization of ROI Size for Development of Computer Assisted
Framework for Breast Tissue Pattern Characterization using
Digitized Screen Film Mammograms
7. Optimization of ANN architecture: A review on nature-inspired
techniques
8. Ensemble Learning Approach to Motor-Imagery EEG Signal
Classification
9. Medical Images Analysis Based on Multi-Label Classification
Methods
10. Figure Search in Biomedical Domain: A Survey of Techniques and
Challenges
11. Application of Machine Learning Algorithms for Classification
and Security of Diagnostic Images
12. Robotics in Healthcare: An Internet of Medical Robotic Things
(IoMRT) Perspective
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
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