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Table of Contents
Intro
Preface
Acknowledgments
Contents
About the Editors
Chapter 1: A Novel Approach for Multiclass Brain Tumour Classification in MR Images
1.1 Introduction
1.1.1 Pre-processing
1.1.2 Feature Extraction
1.1.3 Segmentation
1.1.4 Post-processing
1.2 Related Work
1.2.1 Proposed Algorithm
1.2.2 Feature Extraction Methods
1.2.3 Gray-level Co-occurrence Matrix (GLCM) (Texture)
1.2.4 Gabor Transform (Texture)
1.2.5 Wavelet Transform (Texture)
1.2.6 Support Vector Machine (SVM)
1.3 One-against-one Approach
1.3.1 Fuzzy C Means (FCM)
1.3.2 Fuzzy C-Means Algorithm
1.4 Simulation Results
1.5 Conclusion and Future Work
References
Chapter 2: Chicken Swarm-Based Feature Selection with Optimal Deep Belief Network for Thyroid Cancer Detection and Classificat...
2.1 Introduction
2.2 Related Works
2.3 The Proposed Thyroid Cancer Diagnosis Model
2.3.1 Pre-processing
2.3.2 Process Involved in CSOFS Technique
2.3.3 DBN Classification
2.4 Performance Evaluation
2.5 Conclusion
References
Chapter 3: Efficient Method for the prediction of Thyroid Disease Classification Using Support Vector Machine and Logistic Reg...
3.1 Introduction
3.2 Literature Review
3.3 Proposed Methodology
3.3.1 Dataset Description
3.3.2 Pre-processing
3.3.3 Feature Selection
3.3.4 Classification
3.3.5 Support Vector Machine
3.4 Results and Discussions
3.5 Conclusion
References
Chapter 4: Optimization of Management Response Toward Airborne Infections
4.1 Introduction
4.2 Literature Review
4.3 Background and Key Issues
4.3.1 Proposed Conceptual Framework
4.4 Conclusion
4.4.1 Future Directions
References
Chapter 5: Adaptive Sailfish Optimization-Contrast Limited Adaptive Histogram Equalization (ASFO-CLAHE) for Hyperparameter Tun...
5.1 Introduction
5.2 Literature Survey
5.3 Proposed Methodology
5.3.1 Histogram Equalization
5.3.2 Clipped Histogram Equalization
5.3.3 Adaptive Sailfish Optimization (ASFO)-CLAHE Algorithm
5.4 Performance Measures
5.5 Performance Measures
5.6 Conclusion and Future Work
References
Chapter 6: Efficient Method for Predicting Thyroid Disease Classification using Convolutional Neural Network with Support Vect...
6.1 Introduction
6.2 Literature Review
6.3 Materials and Methods
6.3.1 Dataset Description
6.3.2 Convolutional Neural Network
6.3.3 Support Vector Machine
6.4 Results and Discussions
6.5 Conclusion
References
Chapter 7: Deep Learning in Healthcare Informatics
7.1 Introduction
7.1.1 Healthcare Informatics
7.1.2 History
7.1.3 Need of Healthcare Informatics
7.1.4 Growth of Health Informatics
Preface
Acknowledgments
Contents
About the Editors
Chapter 1: A Novel Approach for Multiclass Brain Tumour Classification in MR Images
1.1 Introduction
1.1.1 Pre-processing
1.1.2 Feature Extraction
1.1.3 Segmentation
1.1.4 Post-processing
1.2 Related Work
1.2.1 Proposed Algorithm
1.2.2 Feature Extraction Methods
1.2.3 Gray-level Co-occurrence Matrix (GLCM) (Texture)
1.2.4 Gabor Transform (Texture)
1.2.5 Wavelet Transform (Texture)
1.2.6 Support Vector Machine (SVM)
1.3 One-against-one Approach
1.3.1 Fuzzy C Means (FCM)
1.3.2 Fuzzy C-Means Algorithm
1.4 Simulation Results
1.5 Conclusion and Future Work
References
Chapter 2: Chicken Swarm-Based Feature Selection with Optimal Deep Belief Network for Thyroid Cancer Detection and Classificat...
2.1 Introduction
2.2 Related Works
2.3 The Proposed Thyroid Cancer Diagnosis Model
2.3.1 Pre-processing
2.3.2 Process Involved in CSOFS Technique
2.3.3 DBN Classification
2.4 Performance Evaluation
2.5 Conclusion
References
Chapter 3: Efficient Method for the prediction of Thyroid Disease Classification Using Support Vector Machine and Logistic Reg...
3.1 Introduction
3.2 Literature Review
3.3 Proposed Methodology
3.3.1 Dataset Description
3.3.2 Pre-processing
3.3.3 Feature Selection
3.3.4 Classification
3.3.5 Support Vector Machine
3.4 Results and Discussions
3.5 Conclusion
References
Chapter 4: Optimization of Management Response Toward Airborne Infections
4.1 Introduction
4.2 Literature Review
4.3 Background and Key Issues
4.3.1 Proposed Conceptual Framework
4.4 Conclusion
4.4.1 Future Directions
References
Chapter 5: Adaptive Sailfish Optimization-Contrast Limited Adaptive Histogram Equalization (ASFO-CLAHE) for Hyperparameter Tun...
5.1 Introduction
5.2 Literature Survey
5.3 Proposed Methodology
5.3.1 Histogram Equalization
5.3.2 Clipped Histogram Equalization
5.3.3 Adaptive Sailfish Optimization (ASFO)-CLAHE Algorithm
5.4 Performance Measures
5.5 Performance Measures
5.6 Conclusion and Future Work
References
Chapter 6: Efficient Method for Predicting Thyroid Disease Classification using Convolutional Neural Network with Support Vect...
6.1 Introduction
6.2 Literature Review
6.3 Materials and Methods
6.3.1 Dataset Description
6.3.2 Convolutional Neural Network
6.3.3 Support Vector Machine
6.4 Results and Discussions
6.5 Conclusion
References
Chapter 7: Deep Learning in Healthcare Informatics
7.1 Introduction
7.1.1 Healthcare Informatics
7.1.2 History
7.1.3 Need of Healthcare Informatics
7.1.4 Growth of Health Informatics