Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
- 218 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
About this book
Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images.- Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges- Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications- Introduces several techniques for medical image processing and analysis for CAD systems design
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Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter 1: Classification of unhealthy and healthy neonates in neonatal intensive care units using medical thermography processing and artificial neural network
- Chapter 2: Use of health-related indices and classification methods in medical data
- Chapter 3: Image analysis for diagnosis and early detection of hepatoprotective activity
- Chapter 4: Characterization of stuttering dysfluencies using distinctive prosodic and source features
- Chapter 5: A deep learning approach for patch-based disease diagnosis from microscopic images
- Chapter 6: A breast tissue characterization framework using PCA and weighted score fusion of neural network classifiers
- Chapter 7: Automated arrhythmia classification for monitoring cardiac patients using machine learning techniques
- Chapter 8: IOT-based fluid and heartbeat monitoring for advanced healthcare
- Index