This book introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary ML/DL research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for healthcare sector, it depth, breadth, complexity, and diversity of this multi-disciplinary area. This book provides a comprehensive overview of Machine Learning (ML) and Deep Learning (DL) algorithms and explores the related use cases in enterprises such as computer aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. The book aims to endow different communities with their innovative advances in theory, analytical results, case studies, numerical simulation, modelling, and computational structuring in the field of ML/DL models for healthcare applications. This book will reveal different dimensions of ML/DL applications and will illustrate its use in the solution of assorted real world biomedical and healthcare problems. This book is a valuable source for information for researchers, scientists, healthcare professional, programmers and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.
Author(s): Om Prakash Jena, Bharat Bhushan, Utku Kose
Series: Biomedical and Robotics Healthcare
Publisher: CRC Press
Year: 2022
Language: English
Pages: 320
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
Chapter 1: Common Data Interface for Sustainable Healthcare System
Chapter 2: Brain–Computer Interface: Review, Applications and Challenges
Chapter 3: Three-Dimensional Reconstruction and Digital Printing of Medical Objects in Purview of Clinical Applications
Chapter 4: Medical Text and Image Processing: Applications, Methods, Issues, and Challenges
Chapter 5: Usage of ML Techniques for ASD Detection: A Comparative Analysis of Various Classifiers
Chapter 6: A Framework for Selection of Machine Learning Algorithms Based on Performance Metrices and Akaike Information Criteria in Healthcare, Telecommunication, and Marketing Sector
Chapter 7: Hybrid Marine Predator Algorithm with Simulated Annealing for Feature Selection
Chapter 8: Survey of Deep Learning Methods in Image Recognition and Analysis of Intrauterine Residues
Chapter 9: A Comprehensive Survey on Breast Cancer Thermography Classification Using Deep Neural Network
Chapter 10: Deep Learning Frameworks for Prediction, Classification and Diagnosis of Alzheimer’s Disease
Chapter 11: Machine Learning Algorithms and COVID-19: A Step for Predicting Future Pandemics with a Systematic Overview
Chapter 12: TRNetCoV: Transferred Learning-based ResNet Model for COVID-19 Detection Using Chest X-ray Images
Chapter 13: The Influence of COVID-19 on Air Pollution and Human Health
Chapter 14: Smart COVID-19 GeoStrategies using Spatial Network Voronoï Diagrams
Chapter 15: Healthcare Providers Recommender System Based on Collaborative Filtering Techniques
Index
