Artificial Intelligence in Biomedical and Modern Healthcare Informatics provides a deeper understanding of current trends in AI and machine learning within health care diagnosis. Its practical approach in healthcare gives insights into wearable sensors and device modules to help doctors and their patients in enhanced healthcare systems. The book helps readers detect common difficulties and their solutions for early detection and prediction of any disease. In 34 chapters, the book provides beginners and experts in the medical science field with general pictures and detailed descriptions of imaging and signal processing principles and clinical applications. With forefront applications and up-to-date analytical methods, this book captures the interests of colleagues in the medical imaging research field and is a valuable resource for healthcare professionals who wish to understand the principles and applications of signal and image processing and its related technologies in healthcare.
Table of Contents
1. Recent Trends in Metabolomics and Artificial Intelligence2. Epilepsy Detection System using CWT and Deep-CNN
3. Isolated Indian Sign Language Recognition with Multihead Attention Transformer based network and Mediapipe’s landmarks
4. Electroencephalography (EEG) and Epilepsy
5. A comprehensive review on state of art imagined speech decoding techniques using Electroencephalography
6. Diagnosis of Parkinson’s Disease based on Biological and Imaging-derived features using Machine learning and Deep learning
7. Impact Of Artificial Intelligence On Public Health: A Prospective Study On Medical Social Work Practice
8. Upshots of Healthcare with AI
9. Parkinson's Disease Diagnosis, Treatment, and Future Scope: An Epilogue
10. Brain Tumor and Feature Detection from MRI and CT scan using Artificial Intelligence
11. Neuromodulation via Brain Stimulation: A Promising Therapeutic Perspective for Alzheimer’s Disease
12. A Biosensor for the Detection of Viruses using One-Dimensional Photonic Crystals
13. Artificial Intelligence Based Seizure Detection Systems in Electroencephalography: Transforming Healthcare for Accurate Diagnosis and Treatment
14. Artificial Intelligence and Image Enhancement based methodologies used for detection of tumor in MRIs of human brain
15. Machine learning based workload Identification using Functional Near-Infrared Spectroscopy (fNIRS) Data
16. Forecasting the COVID-19 pandemic through the hybridization of Machine Intelligent Algorithms
17. Suppression of Noise Signals from Computed Tomography and Ultrasound Medical Images and Performance Evaluation
18. Recent Advances in Removal of Artefacts from EEG Signal Records
19. Prediction Of Non-Alcoholic Fat Liver Disease Using Machine Learning
20. Evaluation of Diabetes Classification with Machine Learning Framework
21. Various Segmentation Methods/ Techniques for Medical Images and The Role of IoT
22. A Review on Brain Computer Interface and its Applications
23. Augmented Mass Detection of Breast Cancer in Mammogram Images Using Deep Intelligent Neural Network Model
24. CNC Machines in Production of Medical Devices
25. Artificial Intelligence and Machine Learning Assisted Robotic Surgery: Current Trends and Future Scope
26. Computer Aided Diagnosis in Health Care: Case Study on Lung Cancer Diagnosis
27. Artificial Intelligence in respiratory diseases with special insight through bioinformatics
28. Analysis and prediction of Cardiomyopathy using Artificial Intelligence
29. A Preemptive Approach to Polycystic Ovary Syndrome Diagnosis using Machine Learning
30. AI and its role in predictive preclinical models for drug efficacy testing
31. Mapping the Landscape of Human Activity Recognition Techniques in Health Monitoring for Chronic Disease Management
32. Analysis and Organization of Mycological Skin Contaminations by Means of Medicinal Imagery
33. A Sensitive Biosensor for the Detection of Blood Components Using 2D Photonic Crystals
34. Machine Learning Assisted EEG Signal Classification for Automated Diagnosis of Mental Stress
35. A Deep Perspective of Blockchain Applications in Healthcare Sector and Industry 4.0
36. Analyzing the role of Machine Learning Techniques in Healthcare Systems
37. CNN based Deep Learning model for Skin Cancer detection using Dermatoscopic Images
38. Bioelectrical Impedance Analysis Body Composition Estimation of Fat Mass Percentage in People with Spinal Cord Injury
39. Advanced EEG Signal Processing and Feature Extraction Concepts
40. Fractal Analysis on Biomedical Signal
41. Detection of Metastasis Osteosarcoma Using Deep Fuzzy Gradient Recurrent Convolutional Neural Network
42. Deep Learning Based Fatigue Detection Using Functional Connectivity
43. Brain Tumor Diagnosis Using Image Classifier
44. Machine Learning-based Solutions for Brain Tumor Detection: Comparative Study and Limitations
45. ISL Recognition System in Realtime using TensorFlow API
46. Indoor and Home-Based Post-Stroke Rehabilitation Techniques- A Systemic Review
47. Exploring the Exciting Potential and Challenges of Brain-Computer Interfaces (BCI)
48. Transmission Dynamics of COVID-19 Virus Disease
50. Design of High Voltage Biphasic Pulse Generation Circuit with 3-Level Isolation Suitable for AED Applications
51. A Novel Scheme of Brain Tumor Detection from MRIs using K-Means Segmentation and Histogram Analysis
52. Analyzing Post COVID-19 Effects on Self-Consciousness and Awareness towards Health: A Neuroscience Framework
53. Crowdsourcing and Artificial Intelligence based Modeling Framework for effective Public Healthcare Informatics and Smart eHealth System
54. Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) in Biomedical Fields: A Prospect in Improvising Medical Healthcare Systems
55. A comprehensive study on implementable antennas for medical applications
56. Deep Learning for Bone Age Assessment: Current Status and Future Prospects
57. Emerging Applications of Artificial Intelligence in Analyzing EEG Signals for the Healthcare Sector
Authors
M. A. Ansari Professor, School of Engineering, Gautam Buddha University, India. 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. R.S Anand Professor, Department of Electrical Engineering ,Indian Institute of Technology, Roorkee, India.R. S. Anand received the B.E., M.E., and Ph.D. degrees from the University of Roorkee, Roorkee, India, in 1985, 1987, and 1992, respectively.,He is currently a Professor with the Electrical Engineering Department, IIT Roorkee, Roorkee. He has authored or coauthored more than 200 research papers in journals and conferences. His current research interests include medical signal and image processing, ultrasonic nondestructive evaluation (NDE), medical diagnosis, and speech signal processing.,Dr. Anand is a Life Member of the Ultrasonic Society of India.
Pragati Tripathi Biomedical Laboratory Department of Electrical Engineering, Gautam Buddha University. India. Pragati Tripathi received an M.Tech degree in power electronics from Gautam Buddha University, Greater Noida, India, in 2018. She is working as a Research Scholar with the School of Engineering, Gautam Buddha University. She has also been associated with IIT Delhi and served as a Research Associate with Sharda University, Greater Noida. Her research interests include signal processing, brain mapping, and neuroscience. Rajat Mehrotra Bajaj Institute of Technology ,GL Bajaj Institute of Technology & Management, Greater Noida, India. 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. Belal Bin Heyat Shenzhen University School of Computer Science and Software Engineering Shenzhen, Guangdong, China.Md Belal Bin Heyat received the B.tech degree in E.I. from Integral University, Lucknow, UP, India in 2014. He is successfully completed Master of Technology degree in Electronics Circuit & System, department of electronics and communication engineering from Integral University, Lucknow, Uttar Pradesh, India in 2016. He has author and co-author in number of International journals, National journals, Symposium and Conferences. He is an editor and reviewer for three international and national journals. His research interests include electronics, communication engineering, instrumentation, therapy, medical and biomedical engineering.
