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Deep Learning for Smart Healthcare

Trends, Challenges and Applications
 
Kiadás sorszáma: 1
Kiadó: Auerbach Publications
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GBP 160.00
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77 280 Ft (73 600 Ft + 5% áfa)
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61 824 (58 880 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 15 456 Ft)
A kedvezmény érvényes eddig: 2024. június 30.
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Rövid leírás:

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.

Hosszú leírás:

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.

Tartalomjegyzék:

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 Patient?s 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.