دانلود کتاب Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

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Cover Machine Learning in Bio-Signal Analysis and Diagnostic Imaging Copyright Contributors Preface 1 Ontology-Based Process for Unstructured Medical Report Mapping Introduction Related Work Ontology-Based Medical Report Mapping Process First OMRMP Phase Second OMRMP Phase Computational System Experimental Setup Results and Discussion Conclusion Relation of the Chapter With the Book References 2 A Computer-Aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images Introduction Human Eye Anatomy and Diabetic Retinopathy Human Eye Anatomy DR Disease The Related Work The Supervised Methods Unsupervised Methods Semiautomated Methods Combining Structure and Color Features The Related Work Results and Discussions The Proposed Multilabel CAD System Phase 1: Color Fundus Image Acquisition Phase 2: Preprocessing Phase 3: Blood Vessels Segmentation Phase 4: Feature Extraction Phase 5: Feature Selection Phase 6: Classification Phase 7: The Evaluation The Experimental Results The Methods and Materials The Results The Discussion The Comparison Between the Presented Methodology and the Others in the Literature Using the Same Dataset Conclusion References Further Reading 3 A DEFS Based System for Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using Ultrasound Images Introduction Data Set Description Clinically Acquired Image Database ROI Selection Protocol and Data Set Distribution Methodology Adopted for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Feature Extraction Module for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Feature Selection Module for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Feature Classification Module for DEFS Based System for the Differential Diagnosis Between Severe Fatty Liver and Cirrhoti ... Performance evaluation of the classification module Experiments and Results Experiment 1: Differential Diagnosis Between Fatty Liver and Cirrhotic Liver Without Using Feature Selection Experiment 2: Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using kNN-DEFS Experiment 3: Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using NB-DEFS Discussion Conclusion and Future Scope Acknowledgments References 4 Infrared Thermography and Soft Computing for Diabetic Foot Assessment Introduction Characteristics of Thermal Infrared Images Spatial Characteristics (Resolution) Noise (Thermal Resolution) Spectral Characteristics Dynamic Range Medical Infrared Thermography Early Diagnosis Using Medical Infrared Thermography How Is IR Thermal Imaging Different From Other Medical Imaging Modalities? Role of Soft Computing in Medical Infrared Thermography Main Focus and Motivation Behind the Chapter Literature Review on Diabetic Foot Complications Assessment Using MIT Methodology Study Population Thermal Image Acquisition and Segmentation Thermal Image Registration Extraction of Region of Interest (ROI) Feature Extraction and Detection of Abnormality Statistical Analysis Classification of Foot for the Assessment of Diabetic Complication Using Deep Learning Neural Network Challenges for Medical Infrared Thermography Thermal Image Acquisition Environmental, Individual, and Technical Challenges Hardware Requirements Specific Challenges to Thermal Imaging Future Roadmap for MIT and Soft Computing Issues to be Addressed Results and Discussion Segmentation Statistical Analysis of the Surface Temperature Distribution (STD) to Detect Abnormality Classification of Foot Using Transfer Learning of Pre-trained CNN Model Future Research Directions on Diabetic Foot Assessment Conclusion Acknowledgments References 5 Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN, and SVM Classifiers Using HRV A ... Introduction Materials and Methods Data Collection and Processing HRV Analysis Classification Module Probabilistic neural network (PNN) classifier K nearest neighbor (KNN) classifier Support vector machine (SVM) classifier Results and Discussion Conclusion References 6 Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization Using ... Introduction Materials and Methods Description of Image Dataset Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization Using D ... ROI extraction phase Feature extraction phase Statistical texture feature models Signal processing based texture feature models Laws mask analysis Transform domain texture feature models Classification Phase 4-Class breast tissue pattern characterization module 2-Class breast tissue pattern characterization module SVM classifier Experiments and Results Experiment 1: Experiment Carried Out for the Selection of Optimum ROI Size for the Development of Computer Assisted Framew ... Experiment 2: Experiment Carried Out for the Selection of Optimum ROI Size for the Development of Computer Assisted Framew ... Statistical Analysis Comparative Analysis Application of the Proposed Work Conclusion and Future Scope Conclusion Future Scope References Further Reading 7 Optimization of ANN Architecture: A Review on Nature-Inspired Techniques Introduction Artificial Neural Network Feedforward Neural Network FNNs structure FNNs learning scheme FNNs error calculation FNNs weight updation Recurrent or Feedback Neural Network Nature Inspired Algorithms Optimization of FNN Nonnature Inspired Algorithm Constructive and pruning Model selection Nature Inspired Algorithms SI for optimizing FNN Bio-inspired but not SI for optimizing FNN Hybrid and some other approaches for optimizing FNN Discussion and Conclusion References 8 Ensemble Learning Approach to Motor Imagery EEG Signal Classification Introduction Human Brain Action Potential Brain Rhythms Electroencephalography Motor Imagery Scope and Relevance Theoretical Background Preprocessing Feature Extraction Wavelet energy and entropy (EngEnt) Bandpower (Bp) Adaptive autoregressive parameters Classification Bagging ensemble learning Adaptive boosting ensemble learning Logistic boosting ensemble learning Background Study Experimental Preparation Dataset Description Experiment I (Exp-I) Experiment II (Exp-II) Experiment III (Exp-III) Experiment IV (Exp-IV) Conclusion References 9 Medical Images Analysis Based on Multilabel Classification Introduction Literature Review Algorithm Adaptation (Direct) Methods Problem Transformation (Indirect) Methods The Hybrid Between Multilabel Classification Methods Literature Results Analysis and Discussion Medical Image Analysis via Multilabel Classification Multilabel CAD System Framework Image Acquisition Preprocessing Feature Extraction Feature Selection Classification An overview of multilabel classification methods Evaluation Challenges of Multilabel Classification High Dimensionality of Data Label Dependency Label Locality Interlabel Similarity Interlabel Diversity The Nature of Multilabel Datasets Scalability Conclusion References Further Reading 10 Figure Retrieval From Biomedical Literature: An Overview of Techniques, Tools, and Challenges Introduction Contextualization and Chapter Organization Image Retrieval: Basic Concepts Content-Based Image Retrieval Workflow in CBIR Major challenges in CBIR Figure Retrieval From Biomedical Papers: Problem Setting Figure Retrieval From Biomedical Papers: Design Aspects Extraction of Figures and Figure Metadata From Research Papers Figure extraction from papers Caption extraction Compound figure detection and separation Extraction of figure text Extraction of mentions Building Figure Representation Visual feature extraction Figure classification Adding semantic features Indexing of Figures Query Processing Some Figure Search Engines in Biomedical Domain GoldMiner FigureSearch Yale Image Finder Open-i Future Directions Conclusion Acknowledgments References 11 Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images Introduction Machine Learning Support Vector Machines Support Vector Regression Neural Networks Application of ML Algorithms in Medical Science Diagnostic Image Classification Using ML Algorithms Diagnostic Image Security Using Watermarking With ML Algorithms Watermarking techniques with NN algorithms Watermarking techniques using SVM algorithms Diagnostic Image Security Using Watermarking With Deep Learning Algorithms Discussion and Future Work Conclusion References 12 Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective Introduction Overview of IoMRT Light Fidelity (Li-Fi) System Architecture IoMRT Sensor/Actuator Layer Network Layer IoMRT Infrastructure Layer Application Layer Li-Fi Technology Connect to IoMRT for Robotic Surgery IoMRT for Robotic Surgery Methodology and Analysis Proposed Robotic Arm for Surgery Hardware Description Software Description Experimental Evaluation Flow Diagram Experimental Analysis Limitations and Research Challenges Computational Problem Optimization Security Concerns of IoMRT Ethical Issue Advantage and Disadvantages of Robotic Surgery With Other Surgeries Applications of Robotics in Healthcare Paradigm Conclusions and Future Enhancement References Index A B C D E F G H I K L M N O P Q R S T U V W Y Back Cover