MARC Record from marc_columbia

LEADER: 13500cam a2200793 i 4500
001 15471416
005 20220730231134.0
006 m o d
007 cr cnu---unuuu
008 201106s2021 flu o 001 0 eng
010 $a 2020049438
035 $a(OCoLC)on1224044362
035 $a(NNC)15471416
040 $aDLC$beng$erda$epn$cDLC$dYDX$dUKAHL$dOCLCF$dTYFRS$dDLC$dOCLCO$dN$T$dSOE$dOCLCO$dYDX$dOCLCO$dOCLCQ
019 $a1242877104$a1258683248
020 $a9781003121152$q(electronic book)
020 $a1003121152$q(electronic book)
020 $a9781000374285$q(electronic book)
020 $a1000374289$q(electronic book)
020 $a9781000374322$q(electronic book)
020 $a1000374327$q(electronic book)
020 $z9780367638832$q(hardcover)
035 $a(OCoLC)1224044362$z(OCoLC)1242877104$z(OCoLC)1258683248
037 $a9781003121152$bTaylor & Francis
042 $apcc
050 00 $aR857.M3$bC66 2021
072 7 $aMED$x009000$2bisacsh
072 7 $aSCI$x055000$2bisacsh
072 7 $aTEC$x059000$2bisacsh
072 7 $aMQW$2bicssc
082 00 $a610.28$223
049 $aZCUA
245 00 $aComputer-aided design and diagnosis methods for biomedical applications /$cedited by Varun Bajaj and G.R. Sinha.
264 1 $aBoca Raton, FL :$bCRC Press,$c2021.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
500 $aIncludes index.
520 $a"The Computer-aided design (CAD) plays key role to improve biomedical system for various applications and helps in detection, identification, predication, analysis and classification of diseases, management of chronic condition, and delivery of health services. Present book discusses uses of CAD to solve real world problems and challenges in Biomedical systems with the help of appropriate case studies and research simulation results. Aiming to overcome the gap between CAD and biomedical science intricacies, it explains behaviours, concepts, fundamentals, principles, case studies and future research directions including automatic identification of related disorders using CAD. Features: Proposes CAD for study of biomedical signals for understanding physiology and to improve healthcare system for diagnosis and identification of health disorders Presents concepts of CAD for biomedical modalities for different disorders Discusses design and simulation examples, issues and challenges Illustrates bio-potential signals and their appropriate analysis for studying different disorders Includes case studies, real-time examples and research directions This volume is aimed at researchers, graduate students in biomedical engineering, image processing, biomedical technology, medical imaging and health informatics"--$cProvided by publisher
505 0 $aCover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- Editors' Biographies -- Contributors -- Chapter 1 Electroencephalogram Signals Based Emotion Classification in Parkinson's Disease Using Recurrence Quantification Analysis and Non-Linear Classifiers -- 1.1 Introduction -- 1.2 Methodology -- 1.2.1 Dataset -- 1.2.2 Pre-Processing -- 1.2.3 Recurrence Quantifcation Analysis -- 1.2.4 Features -- 1.2.5 Classifcation Techniques -- 1.3 Results -- 1.4 Conclusion -- Acknowledgment -- References -- Chapter 2 Sleep Stage Classification Using DWT and Dispersion Entropy Applied on EEG Signals -- 2.1 Introduction -- 2.2 Methodology -- 2.2.1 Sleep-EDF Database -- 2.2.2 Discrete Wavelet Transform -- 2.2.3 Dispersion Entropy and Fluctuation-Based Dispersion Entropy -- 2.2.4 Random Forest Classifers -- 2.3 Results and Discussion -- 2.4 Conclusion -- Acknowledgments -- References -- Chapter 3 Detection of Epileptic Electroencephalogram Signals Employing Visibility Graph Motifs -- 3.1 Introduction -- 3.2 EEG Dataset -- 3.3 Methodology -- 3.3.1 Concept of a Visibility Graph -- 3.3.2 Concept of Horizontal Visibility Graph -- 3.3.3 Concept of Sequential Visibility Graph Motifs -- 3.4 Machine Learning Classifers -- 3.4.1 Random Forest Classifer -- 3.4.2 Support Vector Machine Classifer -- 3.4.3 K-Nearest Neighbor Classifer -- 3.4.4 Naïve-Bayes Classifer -- 3.5 Description of Classifcation Problems -- 3.6 Results and Discussions -- 3.6.1 EEG Signal Analysis Using VG and HVG Motifs -- 3.6.2 Classifcation Performance -- 3.6.3 Comparison with Existing Literatures -- 3.7 Conclusions -- References -- Chapter 4 Effect of Various Standing Poses of Yoga on the Musculoskeletal System Using EMG -- 4.1 Introduction -- 4.2 Standing Poses of Yoga -- 4.2.1 Tadasana (Mountain Pose) -- 4.2.2 Utkatasana (Chair Pose).
505 8 $a4.2.3 Uttanasana (Standing Forward Bend) -- 4.2.4 Vrikshasana (Tree Pose) -- 4.2.5 Utthita Hasta Padangusthasana (Extended Hand-Toe Pose or Standing Big Toe Hold) -- 4.2.6 Garudasana (Eagle Pose) -- 4.2.7 Virabhadrasana (Warrior) -- 4.2.7.1 Virabhadrasana I (Warrior I) -- 4.2.7.2 Virabhadrasana II (Warrior II) -- 4.2.7.3 Virabhadrasana III (Warrior III) -- 4.2.8 Trikonasana (Triangle Pose) -- 4.2.9 Natarajasana (King of the Dance Pose) -- 4.2.10 Prasarita Padottanasana (Wide-Legged Forward Bend) -- 4.2.11 Parsvakonasana (Side Angle Pose) -- 4.2.11.1 Utthita Parsvakonasana (Extended Side Angle Pose) -- 4.2.11.2 Parivrtta Baddha Parsvakonasana (Revolved Side Angle Pose) -- 4.2.12 Ardha Chandrasana (Half-Moon Pose) -- 4.2.13 Plank Pose -- 4.3 Benefts of Standing Poses -- 4.4 Introduction to Electromyography -- 4.5 Analysis of an EMG Signal for Muscle Activity during Standing Poses -- 4.6 Conclusion and Future Scope -- Acknowledgments -- References -- Chapter 5 Early Detection of Parkinson's Disease and SWEDD Using SMOTE and Ensemble Classifier -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Materials and Methods -- 5.3.1 Database and Study Cohort Details -- 5.3.2 Synthetic Minority Oversampling Technique -- 5.3.3 Classifcation Methods -- 5.3.3.1 Naïve Bayes (NB) -- 5.3.3.2 Logistic Regression (LR) -- 5.3.3.3 Support Vector Machines -- 5.3.3.4 Artificial Neural Networks (ANN) -- 5.3.3.5 Decision Tree (C4.5) -- 5.3.3.6 Random Forest (RF) -- 5.3.3.7 Rotation Forest (RotF) -- 5.4 Results and Discussion -- 5.4.1 Experimental Setup -- 5.4.2 Experiment 1: Prediction of Early PD -- 5.4.3 Experiment 2: Discrimination of SWEDDs From Early PDs -- 5.4.4 Discussion -- 5.5 Conclusion -- Acknowledgment -- References -- Chapter 6 Computer-aided Design and Diagnosis Method for Cancer Detection -- 6.1 Introduction.
505 8 $a6.2 CAD-Based Techniques for Early Tumor Detection in Breast Cancer -- 6.2.1 Techniques for Breast Cancer Detection Using CAD -- 6.2.1.1 Image Enhancement Technique -- 6.2.1.2 Edge-Based Segmentation Technique -- 6.2.1.3 Threshold-Based Segmentation Technique -- 6.2.1.4 Clustering-Based Segmentation Technique -- 6.2.2 Feature Extraction Technique -- 6.3 Prostate Cancer Diagnosis: Image-Based CAD -- 6.3.1 Histopathology Image-Based CAD -- 6.3.1.1 Input Modalities -- 6.3.1.2 Prostate Image Segmentation -- 6.3.2 CAD-Based Feature Extraction -- 6.3.3 CAD-Based Classifcation System -- 6.3.3.1 System Accuracy Evaluation -- 6.4 CAD and Its Application Is Another Type of Cancer Diagnosis -- 6.5 Conclusion -- References -- Chapter 7 Automated COVID-19 Detection from CT Images Using Deep Learning -- 7.1 Introduction -- 7.2 Literature Survey in CT Scan Prognosis -- 7.3 Machine Learning Techniques -- 7.3.1 Multilayer Perceptron -- 7.3.2 k-Nearest Neighbor (k-NN) -- 7.3.3 Support Vector Machine (SVM) -- 7.3.4 Random Forest -- 7.3.5 XGBoost -- 7.3.6 Convolutional Neural Network (CNN) -- 7.3.7 Neighborhood Component Analysis -- 7.3.8 Transfer Learning -- 7.4 Results and Discussions -- 7.4.1 Dataset -- 7.4.2 Experimental Setup -- 7.4.3 Performance Metrics -- 7.4.4 Hyper-Parameters Selection -- 7.4.5 Experimental Results -- 7.4.6 Discussion -- 7.5 Conclusion -- References -- Chapter 8 Suspicious Region Diagnosis in the Brain: A Guide to Using Brain MRI Sequences -- 8.1 Introduction -- 8.2 Related Works -- 8.3 Brain MRI Sequences -- 8.4 Discussion -- 8.5 Conclusion -- References -- Chapter 9 Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network -- 9.1 Introduction -- 9.1.1 Heart Ailment -- 9.1.1.1 Coronary Heart Ailment -- 9.1.1.2 Peripheral Artery Diseases -- 9.1.1.3 Innate Heart Ailment.
505 8 $a9.1.1.4 Cardiomyopathy Diseases -- 9.1.1.5 Other Cardiovascular Diseases -- 9.1.2 Fact Removal -- 9.1.2.1 Classifcation Algorithm -- 9.2 Review of Literature -- 9.3 Proposed Methodology -- 9.3.1 Dataset Structure and Description -- 9.3.2 Algorithm for Performance Evaluation -- 9.3.3 Data Collection -- 9.3.4 Data Preprocessing -- 9.3.5 Correlation Matrix -- 9.4 Evaluation and Result -- 9.4.1 Performance Measure -- 9.4.2 Experimental Results -- 9.5 Conclusion and Future Scope -- 9.5.1 Conclusion -- 9.5.2 Future Scope -- References -- Chapter 10 Positioning the Healthcare Client in Diagnostics and the Validation of Care Intensity -- 10.1 Introduction -- 10.2 Diagnostic Process in Change -- 10.2.1 Patient as an Active Partner in Diagnostics -- 10.2.2 Diagnostics as a Key Role in Professional Care Intensity -- 10.2.3 Uncertainty and Too Low Care Intensity in Diagnostics -- 10.2.4 Noticing Groups with a Risk of Under-Diagnosis -- 10.3 Access to Care and Doctor Appointment -- 10.3.1 Awareness of the Problem and Referral for Treatment -- 10.3.2 Cooperative Preparing for a Doctor Appointment -- 10.3.3 Supporting Patients' Self-Care and Preventive Care -- 10.4 Feedback Policy in Diagnostics and Intensity Improvement -- 10.4.1 Hearing Customers' Voice and Experience -- 10.4.2 Feedback Channels and Formats -- 10.4.3 Diffculties in Capturing the Care Intensity Feedback of Patients -- 10.4.4 Feedback as a Normal Procedure -- 10.4.5 Various Areas of Patient Feedback Connecting Care Intensity -- 10.4.6 Just-in-Time Feedback -- 10.4.7 How to Organize and Manage the Intensity-Related Evaluation of Patients -- 10.4.8 How to Maturate Systems for the Intensity Evaluation of Patients -- 10.4.9 Enhancing the Medical Expertise of Clients -- 10.4.10 Consumer-Centered Feedback Systems for Intensity Enhancement -- 10.5 Conclusions -- References.
505 8 $aChapter 11 Computer-aided Diagnosis (CAD) System for Determining Histological Grading of Astrocytoma Based on Ki67 Counting -- 11.1 Background Study -- 11.1.1 Overview of Astrocytoma -- 11.1.2 Overview of Ki67 and Its Characteristics -- 11.1.3 Histological Criteria and Types of Astrocytoma -- 11.2 Previous Studies Related to the Pathological Diagnosis -- 11.2.1 Manual or Clinical Diagnosis -- 11.2.2 Alternative Diagnosis -- 11.3 Computer-aided Diagnosis (CAD) Software -- 11.3.1 Layout of the CAD Software -- 11.3.2 System Operation of the CAD Software -- 11.3.3 Image Acquisition -- 11.3.4 Image Enhancement -- 11.3.5 Color Deconvolution -- 11.3.6 Feature Extraction -- 11.3.7 Counting Ki67 Cells and Tumor Grading Determination -- 11.4 Result and Analysis -- 11.4.1 Image Enhancement Analysis -- 11.4.2 Cell Counting and Ki67 Labeling Index Analysis -- 11.4.3 Tumor Grading Analysis -- 11.5 Conclusion -- Acknowledgment -- References -- Chapter 12 Improved Classification Techniques for the Diagnosis and Prognosis of Cancer -- 12.1 Introduction -- 12.1.1 Medical Services in India -- 12.1.2 Data Mining in Field of HealthCare -- 12.1.3 Architecture for Data Mining -- 12.1.4 Data Mining in Healthcare -- 12.1.4.1 Nature of Healthcare Data -- 12.1.4.2 Patient Data Set -- 12.1.4.3 Preliminary Analysis of Dataset -- 12.1.5 Medical Data Selection and Preparation -- 12.1.6 Issues and Challenges -- 12.1.7 Cancer Treatments Using Decision Support System -- 12.2 Review of Literature -- 12.2.1 Review Process Adapted -- 12.2.2 Categorical Review of Literature -- 12.2.2.1 Literature Review on Algorithm Classifcation -- 12.2.2.2 Literature Review on Cancer Causes and Treatments -- 12.2.2.3 Literature Review on Data Mining in Health Care -- 12.2.2.4 Issue Wise Solution Approach -- 12.3 Problem Statement and Objectives -- 12.3.1 Problem Statement -- 12.3.2 Objectives.
650 0 $aBiomedical materials.
650 0 $aComputer-aided design.
650 0 $aBiomedical engineering.
650 2 $aBiocompatible Materials
650 2 $aBiomedical and Dental Materials
650 2 $aComputer-Aided Design
650 2 $aBiomedical Engineering
650 2 $aBiomedical Technology
650 6 $aBiomatériaux.
650 6 $aConception assistée par ordinateur.
650 6 $aGénie biomédical.
650 7 $acomputer-aided designs (visual works)$2aat
650 7 $abiomedical engineering.$2aat
650 7 $aMEDICAL$xBiotechnology.$2bisacsh
650 7 $aSCIENCE$xPhysics.$2bisacsh
650 7 $aBiomedical engineering.$2fast$0(OCoLC)fst00832568
650 7 $aBiomedical materials.$2fast$0(OCoLC)fst00832586
650 7 $aComputer-aided design.$2fast$0(OCoLC)fst00872701
655 4 $aElectronic books.
700 1 $aBajaj, Varun,$eeditor.
700 1 $aSinha, G. R.,$d1975-$eeditor.
776 08 $iPrint version:$tComputer-aided design and diagnosis methods for biomedical applications.$bFirst edition.$dBoca Raton : CRC Press, 2021$z9780367638832$w(DLC) 2020049437
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15471416$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS