Deep Learning for Smart Healthcare: Trends, Challenges and Applications [Hardcover]

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.

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.

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, clusteringlÃ0