Computational Intelligence in Healthcare Applications

1st Edition - July 14, 2022
Editors: Rajeev Agrawal, M. A. Ansari, R. S. Anand, Sweta Sneha, Rajat Mehrotra
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 0 3 1 - 8
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 3 7 4 - 6

Computational Intelligence in Healthcare Applications discusses a variety of techniques designed to represent, enhance and empower inter-domain research based on computati… Read more

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Computational Intelligence in Healthcare Applications discusses a variety of techniques designed to represent, enhance and empower inter-domain research based on computational intelligence in healthcare. The book serves as a reference for the pervasive healthcare domain which takes into consideration new convergent computing and other applications. The book discusses topics such as mathematical modeling in medical imaging, predictive modeling based on artificial intelligence and deep learning, smart healthcare and wearable devices, and evidence-based predictive modeling. In addition, it discusses computer-aided diagnostic for clinical inferences and pervasive and ubiquitous techniques in healthcare.

This book is a valuable resource for graduate students and researchers in medical informatics, however, it is also ideal for members of the biomedical field and healthcare industry who are interested in learning more about novel technologies and their applications in the field.

Cover image
Title page
Table of Contents
Copyright
Contributors
About the editors
Preface
Acknowledgments
Chapter 1: Clinical decision support systems: Benefits, potential challenges, and applications in pneumothorax segmentation
Abstract
Introduction
Benefits of AI clinical decision support system
Potential challenges associated with AI system
U-Net architecture for the decision support system and clinical diagnosis
Case study on segmentation of pneumothorax from chest X-ray
Comparison of fully connected network (FCN) and U-Net for pneumothorax segmentation
Performance metric
Conclusion
References
Chapter 2: Opportunities and challenges for smart healthcare system in fog computing
Abstract
Introduction
Preliminaries
Related work
Case study
Discussion and open issues
Conclusion
References
Chapter 3: Contemporary overview of bacterial vaginosis in conventional and complementary and alternative medicine
Abstract
Introduction
Epidemiology
Pathophysiology
Clinical features
Diagnosis
Complications
Management
Conclusion
References
Chapter 4: Computer-aided knee joint MR image segmentation—An overview
Abstract
Introduction
Knee MR image segmentation
Configurations with mathematical forms
Classification processes
Conclusion and future trends
References
Chapter 5: Computational approach to assess mucormycosis: A systematic review
Abstract
Introduction
Computational approach in mucormycosis
Mucormycosis association with COVID-19
Diseases act as risk factors of mucormycosis
Statistical analysis for identification of high-risk factors
Conclusion
References
Chapter 6: A review of diabetes management tools and applications
Abstract
Introduction
Background
Review of tools
Discussion
Conclusion
References
Further reading
Chapter 7: Recent advancements of pelvic inflammatory disease: A review on evidence-based medicine
Abstract
Acknowledgments
Introduction
Historical perspectives
Epidemiology
Aetiopathogenesis
Clinical characteristics
Diagnosis
Investigations
Differential diagnosis
Complications
Management
Complementary and alternative medicine (CAM)
Conclusion
References
Chapter 8: A review of amenorrhea toward Unani to modern system with emerging technology: Current advancements, research gap, and future direction
Abstract
Acknowledgments
Introduction
Etiology of the amenorrhea
Unani concept of amenorrhoea (Ihtibas Al-Tamth)
Diagnosis
Treatment
Modern technology
Research gaps and future directions
Conclusion
References
Chapter 9: Wearable EEG technology for the brain-computer interface
Abstract
Introduction
Wireless EEG data acquisition
Brief overview of available wireless EEG headsets and headbands
Electrode-skin impedance
Various electrode technologies used in wearable EEG devices
Summary
References
Chapter 10: Automatic epileptic seizure detection based on the discrete wavelet transform approach using an artificial neural network classifier on the scalp electroencephalogram signal
Abstract
Acknowledgments
Introduction
Materials and methods
Results
Discussion
Conclusion
References
Chapter 11: Event identification by fusing EEG and EMG signals
Abstract
Introduction
Experimental procedure and data analysis
Data processing
Statistical analysis
Results
Conclusion
References
Chapter 12: Hand gesture recognition for the prediction of Alzheimer's disease
Abstract
Alzheimer’s disease and gestures
Literature survey
Proposed model
Result and discussion
Conclusion and future work
References
Chapter 13: A frequency analysis-based apnea detection algorithm using photoplethysmography
Abstract
Introduction
Method of obtaining a PPG
Steps involved in apnea detection through PPG
Results and discussion
Conclusion
References
Chapter 14: Noninvasive health monitoring using bioelectrical impedance analysis
Abstract
Introduction
Principles of bioelectrical impedance analysis (BIA)
Conclusion
References
Chapter 15: Detection of cancer from histopathology medical image data using ML with CNN ResNet-50 architecture
Abstract
Introduction
Related work
Cancer detection process
Application of machine learning in cancer detection
Input dataset
Proposed methodology
Experimental results and discussion
Conclusion
References
Chapter 16: Performance analysis of augmented data for enhanced brain tumor image classification using transfer learning
Abstract
Introduction
Related work
Pipeline and implementation
Dataset description
Results and accuracy
Conclusion and future scope
References
Chapter 17: Brain tumor detection through MRI using image thresholding, k-means, and watershed segmentation
Abstract
Introduction
Literature review
Methodology
Filtration techniques
Segmentation techniques
Feature extraction
Implementation
Results and discussion
Conclusion
Limitations and future scope
References
Chapter 18: An intelligent diagnostic technique using deep convolutional neural network
Abstract
Introduction
Related works
Proposed approach
Dataset used
Experimental results
Discussion
Conclusion
References
Chapter 19: Design of a biosensor for the detection of glucose concentration in urine using 2D photonic crystals
Abstract
Introduction
Design of a biosensor
Simulation and result
Conclusion
References
Chapter 20: Classification of pneumonic infections through chest radiography using textural features analysis and the pattern recognition system
Abstract
Introduction
State of the art
Proposed methodology
Results of network evaluation
Conclusion
References
Chapter 21: Convolutional bi-directional long-short-term-memory based model to forecast COVID-19 in Algeria
Abstract
Acknowledgment
Funding information
Conflicts of interest
Ethical approval
Informed consent
Introduction
Related works
Data sources
Methods
Experiment
Results
Conclusion
References
Index

Rajeev Agrawal

Dr. Rajeev Agrawal holds PhD degree on Computer Science from Jawaharlal Nehru University. He has more than 27 years of experience in teaching and research. He was Head of Computer Science Department at Kumaon Engineering College and currently is Director at GL Bajaj Institute of Technology and Management. He holds four patents, received several grants for research projects and is editorial board member of Health Informatics Journal (Sage). Dr. Agrawal main research interests are m-health, medical imaging and wireless networks

Affiliations and expertise

Director, GL Bajaj Institute of Technology and Management, India

M. A. Ansari

Dr. M.A. Ansari holds PhD degree on Signal and Imaging Processing from Indian Institute of Technology Roorkee. He has 18 years of experience in teaching and research. Currently he is Professor at School of Engineering, Gautam Buddha University, where he supervised 4 PhD and 63 MTech students to date. He authored several book chapters and published almost 30 peer-reviewed articles in international journals. Dr. Ansari main research interests are medical image coding, biomedical instrumentation and control, and digital signal and image processing.

Affiliations and expertise

Professor, School of Engineering, Gautam Buddha University, India

R. S. Anand

R.S. Anand works in the Department of Electrical Engineering at IIT Roorkee, India.

Affiliations and expertise

Department of Electrical Engineering, IIT Roorkee, India

Sweta Sneha

Sweta Sneha works in the Michael J. Coles College of Business at Kennesaw State University, USA.

Affiliations and expertise

Michael J. Coles College of Business, Kennesaw State University, USA

Rajat Mehrotra

Rajat Mehrotra is an Assistant Professor in the Electrical & Electronics Engineering Department at GL Bajaj Institute of Technology & Management, Greater Noida, India. He received his BTech in Electrical and Electronics Engineering from the Dr. A.P.J. Abdul Kalam Technical University, Lucknow (Formerly UPTU), in 2008 and his MTech in Telecommunication Engineering from the same university, in 2014 and his PhD. in the field of Medical Image Processing. His research interests include digital image processing, biomedical imaging, and deep learning. Currently, he is involved in research with the School of Engineering at Gautam Buddha University, Greater Noida. He has published his research in various journals of international repute. He has more than 14 years of experience in teaching and research. He has also published multiple patents in his area of research.

Affiliations and expertise

Bajaj Institute of Technology ,GL Bajaj Institute of Technology & Management, Greater Noida, India