Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Introduction
Editors
Contributors
1. An Introduction to Medical Image Analysis in 3D
	1.1. Introduction
	1.2. Comparison Between 2D and 3D Techniques in Medical Imaging
	1.3. Importance of 3D Medical Image
	1.4. Medical Imaging Types and Modalities
	1.5. Computer Vision System Works in 3D Image Analysis
	1.6. Various Techniques in 3D Image Processing in Medical Imaging
	1.7. Types of Medical Imaging Compressed by 3D Medical Visualization
	1.8. 3D Ultrasound Shortens The Imaging Development
	1.9. Conclusion
	References
2. Automated Epilepsy Seizure Detection from EEG Signals Using Deep CNN Model
	2.1. Introduction
	2.2. Materials and Methodology
		2.2.1. Dataset
		2.2.2. Normalization
		2.2.3. Convolution Neural Network (CNN)
	2.3. Result and Discussions
		2.3.1. Experiment 1: 10-Fold Cross Validation on 90:10 Ratio
		2.3.2. Experiment 2: Training and Testing Ratio Variation
	2.4. Conclusion
	References
3. Medical Image De-Noising Using Combined Bayes Shrink and Total Variation Techniques
	3.1. Introduction
	3.2. Literature Review
	3.3. Theoretical Analysis
		3.3.1. Median Modified Wiener Filter
		3.3.2. Wavelet Transform
		3.3.3. Dual Tree Complex Wavelet Transform
		3.3.4. Sure Shrink
		3.3.5. Bayes Shrink
		3.3.6. Neigh Shrink
		3.3.7. DTCWT Based De-Noising Using Adaptive Thresholding
	3.4. Total Variation Technique
	3.5. Pixel Level DTCWT Image Fusion Technique
	3.6. Performance Evaluation Parameters
		3.6.1. Peak Signal to Noise Ratio
		3.6.2. Structural Similarity Index Matrix
	3.7. Methodology
	3.8. Results And Discussion
	3.9. Conclusions And Future Scope
	References
4. Detection of Nodule and Lung Segmentation Using Local Gabor XOR Pattern in CT Images
	4.1. Introduction
	4.2. Histories
	4.3. Concepts
	4.4. Causes for Lung Cancer
		4.4.1. Smoking
		4.4.2. Familial Predisposition
		4.4.3. Lung Diseases
		4.4.4. Prior Tale Containing Stroke Cancer
		4.4.5. Air Pollution
		4.4.6. Exposure as Far as Engine Exhaust
		4.4.7. Types Containing Tumor
		4.4.8. Signs and Symptoms of Lung Cancer
	4.5. Solution Methodology With Mathematical Formulations
		4.5.1. Feature Extraction
		4.5.2. Modified Area Starting to Be Algorithm
		4.5.3. Gridding
		4.5.4. Selection of Seed Point
	4.6. Morphological Operation
	4.7. Conclusions and Future Work
	References
5. Medical Image Fusion Using Adaptive Neuro Fuzzy Inference System
	5.1. Introduction
		5.1.1. Overview
			5.1.1.1. Digital Image
			5.1.1.2. Types of Digital Images
			5.1.1.2.1. Binary Images
			5.1.1.2.2. Grayscale Image
			5.1.1.2.3. Color Image
			5.1.1.3. Medical Imaging Type
			5.1.1.3.1. CT Images
			5.1.1.3.2. MRI Image
			5.1.1.4. Image Fusion
			5.1.1.4.1. Some Meanings of Fusion
			5.1.1.4.2. Applications of Image Fusion
			5.1.1.4.3. Medical Image Fusion
		5.1.2. Literature Survey
			5.1.2.1. A Brief History about Literature Survey
		5.1.3. Solution Methodology
			5.1.3.1. Fuzzy Logic
			5.1.3.2. Fuzzy Set
			5.1.3.3. Membership Functions
			5.1.3.4. Fuzzy Inference System
		5.1.4. Proposed Methodology
			5.1.4.1. Applying to ANFIS
			5.1.4.1.1. ANFIS Rule
			5.1.4.1.2. RULES:
			5.1.4.1.3. Merge Color Channel
		5.1.5. Result and Discussion
			5.1.5.1. Simulation Result
			5.1.5.2. Performance Analysis
		5.1.6. Conclusion and Future Scope
			5.1.6.1. Future Scope
	References
6. Medical Imaging in Healthcare Applications
	6.1. Introduction
	6.2. Image Modalities
		6.2.1. PET Scan
		6.2.2. Ultrasound
		6.2.3. MRI Scan
		6.2.4. CT Scan
	6.3. Recent Trends in Healthcare Technology
	6.4. Scope for Future Work
	6.5. Conclusions
	References
7. Classification of Diabetic Retinopathy by Applying an Ensemble of Architectures
	7.1. Introduction
		7.1.1. Literature Survey
	7.2. Method and Data
		7.2.1. Dataset Used
		7.2.2. Augmentation of Dataset
		7.2.3. Partition of Dataset
		7.2.4. Evaluation Metrics
		7.2.5. Method
	7.3. Results
	7.4. Conclusion
	References
8. Compression of Clinical Images Using Different Wavelet Function
	8.1. Introduction: Background and Need of Compression
	8.2. Terminology UtilizeD for Implementation
	8.3. Proposed Algorithm
		8.3.1. Calculation for Picture Compression Utilizing Wavelet
			8.3.1.1. Input Image
			8.3.1.2. Compression Decompression and Filters
			8.3.1.3. Compression
			8.3.1.4. Image Reconstruction
		8.3.2. Performance Analysis
	8.4. Implementation and Result
		8.4.1. Analysis of CT Scan Images
			8.4.1.1. Wavelet Haar Function Is Used
	8.5. Conclusion
	References
9. PSO-Based Optimized Machine Learning Algorithms for the Prediction of Alzheimer's Disease
	9.1. Introduction
	9.2. Related Work
	9.3. Material and Methods
		9.3.1. Proposed Workflow
		9.3.2. Database
		9.3.3. Data Pre-processing
	9.4. Particle Swarm Optimization (PSO) Techniques
		9.4.1. Machine Learning Models
	9.5. Experimental Results
	9.6. Discussion
	9.7. Conclusion
	References
10. Parkinson's Disease Detection Using Voice Measurements
	10.1. Introduction
	10.2. Literature Survey
		10.2.1. Parkinson's Syndromes
		10.2.2. Symptoms
		10.2.3. Causes
		10.2.4. Threat Causes
		10.2.5. Complications
	10.3. Methodologies Used in Present Work
		10.3.1. Machine Learning (ML) and Artificial Intelligence (AI)
		10.3.2. Ensemble Learning
		10.3.3. Advantages
		10.3.4. Data Drive Machine Learning
		10.3.5. Architecture
	10.4. Proposed System
	10.5. Testing
		10.5.1. Type of Testing
		10.5.2. Integration Testing
		10.5.3. Functional Testing
	10.6. Conclusion and Future Enhancements
	References
11. Speech Impairment Using Hybrid Model of Machine Learning
	11.1. Introduction
	11.2. Types of Classifier
		11.2.1. Naive Bayes (Classifier)
		11.2.2. Support Vector Machine (SVM)
		11.2.3. K-Nearest Neighbor (KNN)
		11.2.4. Decision Tree
		11.2.5. Random Forest
		11.2.6. XGBoost
		11.2.7. Extra Trees
	11.3. Related Work
	11.4. Proposed Work
	11.5. Results and Discussions
	11.6. Conclusion
	References
12. Advanced Ensemble Machine Learning Model for Balanced BioAssays
	12.1. Introduction
	12.2. Related Work
	12.3. Proposed Work
		12.3.1. Ensemble Classification
	12.4. Experimental Investigation
		12.4.1. Dataset Report
		12.4.2. Experimental Setting
	12.5. Results
		12.5.1. Assessment of Results
		12.5.2. Assessment of the Model on the Dataset
	12.6. Conclusion
	References
13. Lung Segmentation and Nodule Detection in 3D Medical Images Using Convolution Neural Network
	13.1. Introduction
	13.2. Review of Literature
	13.3. Rationale of the Study
		13.3.1. Morphological Processing of the Digital Image
	13.4. Objectives of Study
	13.5. Proposed Methodology
		13.5.1. Evaluation Results for Medical Image Handling
			13.5.1.1. False Positive Rate (FPR)
			13.5.1.2. False Negative Rate (FNR)
			13.5.1.3. Sensitivity
			13.5.1.4. Specificity
			13.5.1.5. Accuracy
	13.6. Expected Outcome of Research Work
	13.7. Conclusion and Future work
	References
Index