About the Book:
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/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.
This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems.
Features:
Covers the fundamentals of ML and DL in the context of healthcare applications
Discusses various data collection approaches from various sources and how to use them in ML/DL models
Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field
Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics
Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly
Table of Contents
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
