| ISBN13: | 9780367638849 |
| ISBN10: | 0367638843 |
| Binding: | Paperback |
| No. of pages: | 392 pages |
| Size: | 234x156 mm |
| Weight: | 453 g |
| Language: | English |
| Illustrations: | 105 Illustrations, black & white; 4 Halftones, black & white; 101 Line drawings, black & white; 77 Tables, black & white |
| 700 |
Computer-aided Design and Diagnosis Methods for Biomedical Applications
GBP 45.99
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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. It explains behaviours, concepts, fundamentals, principles, case studies and future research directions including automatic identification of related disorders using CAD.
Computer-aided design (CAD) plays a key role in improving biomedical systems for various applications. It also helps in the detection, identification, predication, analysis, and classification of diseases, in the management of chronic conditions, and in the delivery of health services. This book discusses the 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, it describes behaviors, concepts, fundamentals, principles, case studies, and future directions for research, including the automatic identification of related disorders using CAD.
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Features:
Proposes CAD for the study of biomedical signals to understand physiology and to improve healthcare systems? ability to diagnose and identify health disorders.
Presents concepts of CAD for biomedical modalities in different disorders.
Discusses design and simulation examples, issues, and challenges.
Illustrates bio-potential signals and their appropriate use in studying different disorders.
Includes case studies, practical examples, and research directions.
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Computer-Aided Design and Diagnosis Methods for Biometrical Applications is aimed at researchers, graduate students in biomedical engineering, image processing, biomedical technology, medical imaging, and health informatics.
Chapter 1 Electroencephalogram Signals Based Emotion Classification in Parkinson?s Disease Using Recurrence Quantification Analysis and Non-Linear Classifiers
Chapter 2 Sleep Stage Classification Using DWT and Dispersion Entropy Applied on EEG Signals
Chapter 3 Detection of Epileptic Electroencephalogram Signals Employing Visibility Graph Motifs
Chapter 4 Effect of Various Standing Poses of Yoga on the Musculoskeletal System Using EMG
Chapter 5 Early Detection of Parkinson Disease and SWEDD Using SMOTE and Ensemble
Chapter 6 Computer-Aided Design and Diagnosis Method for Cancer Detection
Chapter 7 Automated COVID-19 Detection from CT Images Using Deep Learning
Chapter 8 Suspicious Region Diagnosis in the Brain: A Guide to Using Brain MRI Sequences
Chapter 9 Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network
Chapter 10 Positioning the Healthcare Client in Diagnostics and the Validation of Care Intensity
Chapter 11 Computer-Aided Diagnosis (CAD) System for Determining Histological Grading of Astrocytoma Based on Ki67 Counting
Chapter 12 Improved Classification Techniques for the Diagnosis and Prognosis of Cancer
Chapter 13 Discovery of Thyroid Disease Using Different Ensemble Methods with Reduced Error Pruning Technique
Chapter 14 Reliable Diagnosis and Prognosis of COVID-19
Chapter 15 Computer-Aided Diagnosis Methods for Non-Invasive Imaging of Sub-Skin Lesions
Index