The book is a baseline reference for researchers and academicians who are investigating the application of deep learning algorithms in the healthcare sector. It focuses on medical imaging and healthcare data analytics.
Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data.
Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process.
Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.
Preface. List of Contributors.
Chapter 1 Deep Learning in Healthcare and
Clinical Studies.
Chapter 2 Deep Learning Framework for Classification of
Healthcare Data.
Chapter 3 Leveraging Deep Learning in Hate Speech Analysis
on Social Platform.
Chapter 4 Medical Image Analysis Based on Deep Learning
Approach for Early Diagnosis of Diseases.
Chapter 5 A Study of Medical Image
Analysis using Deep Learning Approaches.
Chapter 6 Deep Learning for
Designing Heuristic Methods for Healthcare Data Analytics.
Chapter 7 Deep
Learning-Based Smart Healthcare System for Patients Discomfort Detection.
Chapter 8 Gesture Identification for Hearing-Impaired through Deep Learning.
Chapter 9 Deep Learning-Based Cloud Computing Technique for Patient Data
Management.
Chapter 10 Challenges and Issues in Health Care and Clinical
Studies Using Deep Learning.
Chapter 11 Protecting Medical Images Using Deep
Learning Fuzzy Extractor Model.
Chapter 12 Review of Various Deep Learning
Techniques with a Case Study on Prognosticate Diagnostics of Liver Infection.
Chapter 13 Case Study: Application of Ensemble Classifier for Diabetes
Healthcare Data Analytics.
Chapter 14 Deep Convolutional Neural Network
Models for Early Detection of Breast Cancer from Digital Mammograms.
Chapter
15 Case Study: Deep Learning-Based Approach for Detection and Treatment of
Retinopathy of Prematurity. Index.
Dr. K. Murugeswari is a Senior Assistant Professor in the School of Computing Science and Engineering at VIT Bhopal University, M.P, India.
Dr. B. Sundaravadivazhagan is a professor in the Department of Information Technology at the University of Technology and Applied Science-AL Mussanah in Oman.
Dr. S. Poonkuntran is a Professor and Dean in the School of Computing Science and Engineering at VIT Bhopal Uniersity, M.P, India.
Dr. Thendral Puyalnithi is an Assistant Professor Senior in the Mepco Schlenk Engineering College in the Department of Artificial Intelligence and Data Science, Tamil Nadu, India.
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