Computational Analysis and Deep Learning for Medical Care

Computational Analysis and Deep Learning for Medical Care

by Amit Kumar Tyagi
Released August 2021
Publisher(s): Wiley-Scrivener
ISBN: 9781119785729

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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

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. Part 1: Deep Learning and Its Models
    1. 1 CNN: A Review of Models, Application of IVD Segmentation
      1. 1.1 Introduction
      2. 1.2 Various CNN Models
      3. 1.3 Application of CNN to IVD Detection
      4. 1.4 Comparison With State-of-the-Art Segmentation Approaches for Spine T2W Images
      5. 1.5 Conclusion
      6. References
    2. 2 Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective
      1. 2.1 Introduction
      2. 2.2 Related Work
      3. 2.3 Artificial Intelligence Perspective
      4. 2.4 Architecture
      5. 2.5 Conclusion
      6. References
    3. 3 Identification of a Suitable Transfer Learning Architecture for Classification: A Case Study with Liver Tumors
      1. 3.1 Introduction
      2. 3.2 Related Works
      3. 3.3 Convolutional Neural Networks
      4. 3.4 Transfer Learning
      5. 3.5 System Model
      6. 3.6 Results and Discussions
      7. 3.7 Conclusion
      8. References
    4. 4 Optimization and Deep Learning-Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images
      1. 4.1 Introduction
      2. 4.2 Related Works
      3. 4.3 Proposed Method
      4. 4.4 Results and Discussion
      5. 4.5 Conclusion
      6. References
  6. Part 2: Applications of Deep Learning
    1. 5 Deep Learning for Clinical and Health Informatics
      1. 5.1 Introduction
      2. 5.2 Related Work
      3. 5.3 Motivation
      4. 5.4 Scope of the Work in Past, Present, and Future
      5. 5.5 Deep Learning Tools, Methods Available for Clinical, and Health Informatics
      6. 5.6 Deep Learning: Not-So-Near Future in Biomedical Imaging
      7. 5.7 Challenges Faced Toward Deep Learning Using in Biomedical Imaging
      8. 5.8 Open Research Issues and Future Research Directions in Biomedical Imaging (Healthcare Informatics)
      9. 5.9 Conclusion
      10. References
    2. 6 Biomedical Image Segmentation by Deep Learning Methods
      1. 6.1 Introduction
      2. 6.2 Overview of Deep Learning Algorithms
      3. 6.3 Other Deep Learning Architecture
      4. 6.4 Biomedical Image Segmentation
      5. 6.5 Conclusion
      6. References
    3. 7 Multi-Lingual Handwritten Character Recognition Using Deep Learning
      1. 7.1 Introduction
      2. 7.2 Related Works
      3. 7.3 Materials and Methods
      4. 7.4 Experiments and Results
      5. 7.5 Conclusion
      6. References
    4. 8 Disease Detection Platform Using Image Processing Through OpenCV
      1. 8.1 Introduction
      2. 8.2 Problem Statement
      3. 8.3 Conclusion
      4. 8.4 Summary
      5. References
    5. 9 Computer-Aided Diagnosis of Liver Fibrosis in Hepatitis Patients Using Convolutional Neural Network
      1. 9.1 Introduction
      2. 9.2 Overview of System
      3. 9.3 Methodology
      4. 9.4 Performance and Analysis
      5. 9.5 Experimental Results
      6. 9.6 Conclusion and Future Scope
      7. References
  7. Part 3: Future Deep Learning Models
    1. 10 Lung Cancer Prediction in Deep Learning Perspective
      1. 10.1 Introduction
      2. 10.2 Machine Learning and Its Application
      3. 10.3 Related Work
      4. 10.4 Why Deep Learning on Top of Machine Learning?
      5. 10.5 How is Deep Learning Used for Prediction of Lungs Cancer?
      6. 10.6 Conclusion
      7. References
    2. 11 Lesion Detection and Classification for Breast Cancer Diagnosis Based on Deep CNNs from Digital Mammographic Data
      1. 11.1 Introduction
      2. 11.2 Background
      3. 11.3 Methods
      4. 11.4 Application of Deep CNN for Mammography
      5. 11.5 System Model and Results
      6. 11.6 Research Challenges and Discussion on Future Directions
      7. 11.7 Conclusion
      8. References
    3. 12 Health Prediction Analytics Using Deep Learning Methods and Applications
      1. 12.1 Introduction
      2. 12.2 Background
      3. 12.3 Predictive Analytics
      4. 12.4 Deep Learning Predictive Analysis Applications
      5. 12.5 Discussion
      6. 12.6 Conclusion
      7. References
    4. 13 Ambient-Assisted Living of Disabled Elderly in an Intelligent Home Using Behavior Prediction—A Reliable Deep Learning Prediction System
      1. 13.1 Introduction
      2. 13.2 Activities of Daily Living and Behavior Analysis
      3. 13.3 Intelligent Home Architecture
      4. 13.4 Methodology
      5. 13.5 Senior Analytics Care Model
      6. 13.6 Results and Discussions
      7. 13.7 Conclusion
      8. Nomenclature
      9. References
    5. 14 Early Diagnosis Tool for Alzheimer’s Disease Using 3D Slicer
      1. 14.1 Introduction
      2. 14.2 Related Work
      3. 14.3 Existing System
      4. 14.4 Proposed System
      5. 14.5 Results and Discussion
      6. 14.6 Conclusion
      7. References
  8. Part 4: Deep Learning - Importance and Challenges for Other Sectors
    1. 15 Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities
      1. 15.1 Introduction
      2. 15.2 Related Work
      3. 15.3 Development of Personalized Medicine Using Deep Learning: A New Revolution in Healthcare Industry
      4. 15.4 Deep Learning Applications in Precision Medicine
      5. 15.5 Deep Learning for Medical Imaging
      6. 15.6 Drug Discovery and Development: A Promise Fulfilled by Deep Learning Technology
      7. 15.7 Application Areas of Deep Learning in Healthcare
      8. 15.8 Privacy Issues Arising With the Usage of Deep Learning in Healthcare
      9. 15.9 Challenges and Opportunities in Healthcare Using Deep Learning
      10. 15.10 Conclusion and Future Scope
      11. References
    2. 16 A Perspective Analysis of Regularization and Optimization Techniques in Machine Learning
      1. 16.1 Introduction
      2. 16.2 Regularization in Machine Learning
      3. 16.3 Convexity Principles
      4. 16.4 Conclusion and Discussion
      5. References
    3. 17 Deep Learning-Based Prediction Techniques for Medical Care: Opportunities and Challenges
      1. 17.1 Introduction
      2. 17.2 Machine Learning and Deep Learning Framework
      3. 17.3 Challenges and Opportunities
      4. 17.4 Clinical Databases—Electronic Health Records
      5. 17.5 Data Analytics Models—Classifiers and Clusters
      6. 17.6 Deep Learning Approaches and Association Predictions
      7. 17.7 Conclusion
      8. 17.8 Applications
      9. References
    4. 18 Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years
      1. 18.1 Introduction
      2. 18.2 Evolution of Machine Learning and Deep Learning
      3. 18.3 The Forefront of Machine Learning Technology
      4. 18.4 The Challenges Facing Machine Learning and Deep Learning
      5. 18.5 Possibilities With Machine Learning and Deep Learning
      6. 18.6 Potential Limitations of Machine Learning and Deep Learning
      7. 18.7 Conclusion
      8. Acknowledgement
      9. Contribution/Disclosure
      10. References
    5. Index

Product information

  • Title: Computational Analysis and Deep Learning for Medical Care
  • Author(s): Amit Kumar Tyagi
  • Release date: August 2021
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119785729