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

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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