Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications
- Available for pre-order. Item will ship after January 28, 2022
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.
Table of Contents
1. Common Data Interface for Sustainable Healthcare System.
Abhilash C B, K.T Deepak, Rajendra Hegadi and Kavi Mahesh
2. Brain-Computer Interface: Review, Applications and Challenges.
Prashant Sengar and Shawli Bardhan
3. Three Dimensional Reconstruction And Digital Printing Of 3D Medical Objects In Purview Of Clinical Applications.
Sushitha Susan Joseph and Aju D
4. Medical Text And Image Processing: Applications, Issues And Challenges.
Behzad Soleimani Neysiani and Hassan Homayoun
5. Usage Of Ml Techniques For Asd Detection: A Comparative Analysis Of Various Classifiers.
Ashima Sindhu Mohanty, Priyadarsan Parida and Krushna Chandra Patra
6. A Framework For Selection Of Machine Learning Algorithms Based On Performance Metrices And Akaike Information Criteria In Healthcare, Telecommunication, And Marketing Sector.
Abubakar Kamagata Hamisu and Jasleen Kaur
7. An Improved Marine Predators’ Algorithm With Simulated Annealing For Feature Selection In The Medical Field.
Utkarsh Khaire, R Dhanalakshmi and Balakrishnan K
8. Survey Of Deep Learning Methods In Image Recognition And Analysis Of Intrauterine Residues.
Bhawna Swarnkar, Dr. Nilay Khare and Dr. Manasi Gyanchandani
9. A Comprehensive Survey On Breast Cancer Thermography Classification Using Deep Neural Network.
Amira Hassan Abed, Essam M Shaaban, Om Prakash Jena, Ahmed A. Elngar
10. Deep Learning Frameworks For Prediction, Classification And Diagnosis Of Alzheimer’s Disease.
Nitin Singh Rajput, Mithun Singh Rajput and Purnima Dey Sarkar
11. Machine Learning Algorithms And Covid-19; A Step For Predicting Future Pandemics: A Systematic Overview.
Madhumita Pal, Kuldeep Dhama, Smita Parija, Om Prakash Jena and Ranjan K. Mohapatra
12. Trnetcov: Transferred Learning Based Resnet Model For Covid-19 Detection Using Chest X-Ray Images.
G V Eswara Rao and Rajitha B
13. The Influence Of Covid-19 On Air Pollution And Human Health.
Loubna Bouhachlaf, Jamal Mabrouki and Souad El Hajjaji
14. Smart Covid-19 Geostrategies Using Spatial Network Voronoï Diagrams.
Aziz Mabrouk and Azedine Boulmakoul
15. Healthcare Providers Recommender System Based On Collaborative Filtering Techniques
Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.
Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at School of Engineering and Technology, Sharda University, Greater Noida, India.
Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.