Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications

by D. Jude Hemanth, Deepak Gupta, Valentina Emilia Balas
Released March 2019
Publisher(s): Academic Press
ISBN: 9780128156438

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Book description

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers, scientists, engineers and practitioners interested in intelligent data analysis, knowledge discovery, and decision support in databases.

  • Provides the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection
  • Contains an analysis of medical databases to provide diagnostic expert systems
  • Addresses the integration of intelligent data analysis techniques within biomedical information systems

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of Contributors
  6. Chapter 1. IoT-Based Intelligent Capsule Endoscopy System: A Technical Review
    1. Abstract
    2. 1.1 Introduction
    3. 1.2 Data Acquisition
    4. 1.3 On-Chip Data-Processing Unit
    5. 1.4 Data Management of Wireless Capsule Endoscopy Systems
    6. 1.5 IoT-Based Wireless Capsule Endoscopy System
    7. 1.6 Future Challenges
    8. 1.7 Conclusion
    9. References
  7. Chapter 2. Optimization of Methods for Image-Texture Segmentation Using Ant Colony Optimization
    1. Abstract
    2. 2.1 Introduction
    3. 2.2 Implementation of Ant Colony Optimization Algorithm
    4. 2.3 Image Segmentation Techniques
    5. 2.4 Evaluation of Segmentation Techniques
    6. 2.5 Experiments and Results
    7. 2.6 Conclusion
    8. References
    9. Further Reading
  8. Chapter 3. A Feature Fusion-Based Discriminant Learning Model for Diagnosis of Neuromuscular Disorders Using Single-Channel Needle Electromyogram Signals
    1. Abstract
    2. 3.1 Introduction
    3. 3.2 State-of-Art-Methods
    4. 3.3 Theoretical Modeling of Learning from Big Data
    5. 3.4 Medical Measurements and Data Analysis
    6. 3.5 Results and Discussion
    7. 3.6 Conclusion
    8. References
    9. Further Reading
  9. Chapter 4. Evolution of Consciousness Systems With Bacterial Behaviour
    1. Abstract
    2. 4.1 Introduction
    3. 4.2 Proposal
    4. 4.3 Testing
    5. 4.4 Conclusions and Future Work
    6. References
    7. Further Reading
  10. Chapter 5. Analysis of Transform-Based Compression Techniques for MRI and CT Images
    1. Abstract
    2. 5.1 Introduction
    3. 5.2 Proposed Methods
    4. 5.3 Results and Discussion
    5. 5.4 Conclusion
    6. Acknowledgement
    7. References
  11. Chapter 6. A Medical Image Retrieval System in PACS Environment for Clinical Decision Making
    1. Abstract
    2. 6.1 Introduction
    3. 6.2 Proposed Integrated Framework
    4. 6.3 Phase II: Text-Based Image Retrieval System
    5. 6.4 Summary and Conclusion
    6. References
    7. Further Reading
  12. Chapter 7. A Neuro-Fuzzy Inference Model for Diabetic Retinopathy Classification
    1. Abstract
    2. 7.1 Introduction
    3. 7.2 Related Work
    4. 7.3 Methodology
    5. 7.4 Empirical Studies
    6. 7.5 Result and Discussion
    7. 7.6 Conclusion
    8. References
  13. Chapter 8. Computational Automated System for Red Blood Cell Detection and Segmentation
    1. Abstract
    2. 8.1 Introduction
    3. 8.2 Complete Blood Count
    4. 8.3 Red Blood Cell Segmentation
    5. 8.4 Related Work
    6. 8.5 Methodology
    7. 8.6 Proposed Method for Red Blood Cell Detection and Segmentation
    8. 8.7 Developed Graphical User Interface
    9. 8.8 Results and Discussion
    10. 8.9 Conclusion
    11. References
  14. Chapter 9. Evolutionary Algorithm With Memetic Search Capability for Optic Disc Localization in Retinal Fundus Images
    1. Abstract
    2. 9.1 Introduction
    3. 9.2 Classical Differential Evolution for Optic Disc Localization
    4. 9.3 Memetic Differential Evolution
    5. 9.4 Performance Analysis
    6. 9.5 Conclusion
    7. References
  15. Chapter 10. Classification of Myocardial Ischemia in Delayed Contrast Enhancement Using Machine Learning
    1. Abstract
    2. 10.1 Introduction
    3. 10.2 Related Work
    4. 10.3 Methodology
    5. 10.4 Conclusion
    6. References
  16. Chapter 11. Simple-Link Sensor Network-Based Remote Monitoring of Multiple Patients
    1. Abstract
    2. 11.1 Introduction
    3. 11.2 Related Works
    4. 11.3 The Scenario of Proposed System
    5. 11.4 Results and Discussion
    6. 11.5 Life Expectancy of Wireless Portable Sensor Unit
    7. 11.6 Maximum Transmission Between the Wireless Portable Sensor Unit and Wireless Access Point
    8. 11.7 Conclusion
    9. References
  17. Chapter 12. Hybrid Approach for Classification of Electroencephalographic Signals Using Time–Frequency Images With Wavelets and Texture Features
    1. Abstract
    2. 12.1 Introduction
    3. 12.2 Methodology
    4. 12.3 Results and Discussion
    5. 12.4 Conclusion
    6. References
  18. Index

Product information

  • Title: Intelligent Data Analysis for Biomedical Applications
  • Author(s): D. Jude Hemanth, Deepak Gupta, Valentina Emilia Balas
  • Release date: March 2019
  • Publisher(s): Academic Press
  • ISBN: 9780128156438