Book description
This book discuss how deep learning can help healthcare images or text data in making useful decisions”. For that, the need of reliable deep learning models like Neural networks, Convolutional neural network, Backpropagation, Recurrent neural network is increasing in medical image processing, i.e., in Colorization of Black and white images of X-Ray, automatic machine translation, object classification in photographs / images (CT-SCAN), character or useful generation (ECG), image caption generation, etc. Hence, Reliable Deep Learning methods for perception or producing belter results are highly effective for e-healthcare applications, which is the challenge of today. For that, this book provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, this book will cover introduction, requirement, importance, issues and challenges, etc., faced in available current deep learning models (also include innovative deep learning algorithms/ models for curing disease in Medicare) and provide opportunities for several research communities with including several research gaps in deep learning models (for healthcare applications).
Table of contents
- Cover
- Title Page
- Copyright
- Preface
-
Part 1: Deep Learning and Its Models
- 1 CNN: A Review of Models, Application of IVD Segmentation
- 2 Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective
- 3 Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors
- 4 Optimization and Deep Learning-Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images
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Part 2: Applications of Deep Learning
-
5 Deep Learning for Clinical and Health Informatics
- 5.1 Introduction
- 5.2 Related Work
- 5.3 Motivation
- 5.4 Scope of the Work in Past, Present, and Future
- 5.5 Deep Learning Tools, Methods Available for Clinical, and Health Informatics
- 5.6 Deep Learning: Not-So-Near Future in Biomedical Imaging
- 5.7 Challenges Faced Toward Deep Learning Using in Biomedical Imaging
- 5.8 Open Research Issues and Future Research Directions in Biomedical Imaging (Healthcare Informatics)
- 5.9 Conclusion
- References
- 6 Biomedical Image Segmentation by Deep Learning Methods
- 7 Multi-Lingual Handwritten Character Recognition Using Deep Learning
- 8 Disease Detection Platform Using Image Processing Through OpenCV
- 9 Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network
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5 Deep Learning for Clinical and Health Informatics
-
Part 3: Future Deep Learning Models
- 10 Lung Cancer Prediction in Deep Learning Perspective
- 11 Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data
- 12 Health Prediction Analytics Using Deep Learning Methods and Applications
- 13 Ambient-Assisted Living of Disabled Elderly in an Intelligent Home Using Behavior Prediction—A Reliable Deep Learning Prediction System
- 14 Early Diagnosis Tool for Alzheimer’s Disease Using 3D Slicer
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Part 4: Deep Learning - Importance and Challenges for Other Sectors
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15 Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities
- 15.1 Introduction
- 15.2 Related Work
- 15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry
- 15.4 Deep Learning Applications in Precision Medicine
- 15.5 Deep Learning for Medical Imaging
- 15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology
- 15.7 Application Areas of Deep Learning in Healthcare
- 15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare
- 15.9 Challenges and Opportunities in Healthcare Using Deep Learning
- 15.10 Conclusion and Future Scope
- References
- 16 A Perspective Analysis of Regularization and Optimization Techniques in Machine Learning
-
17 Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges
- 17.1 Introduction
- 17.2 Machine Learning and Deep Learning Framework
- 17.3 Challenges and Opportunities
- 17.4 Clinical Databases—Electronic Health Records
- 17.5 Data Analytics Models—Classifiers and Clusters
- 17.6 Deep Learning Approaches and Association Predictions
- 17.7 Conclusion
- 17.8 Applications
- References
-
18 Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years
- 18.1 Introduction
- 18.2 Evolution of Machine Learning and Deep Learning
- 18.3 The Forefront of Machine Learning Technology
- 18.4 The Challenges Facing Machine Learning and Deep Learning
- 18.5 Possibilities With Machine Learning and Deep Learning
- 18.6 Potential Limitations of Machine Learning and Deep Learning
- 18.7 Conclusion
- Acknowledgement
- Contribution/Disclosure
- References
- Index
-
15 Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities
Product information
- Title: Computational Analysis and Deep Learning for Medical Care
- Author(s):
- Release date: August 2021
- Publisher(s): Wiley-Scrivener
- ISBN: 9781119785729
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