Machine Learning in Bio-Signal Analysis and Diagnostic Imaging /
Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along...
| Main Authors: | , , , , |
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| Format: | software, multimedia |
| Language: | eng |
| Published: |
London : Academic Press,
2019
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| Subjects: | |
| Online Access: | https://www.sciencedirect.com/science/book/9780128160862 |
| _version_ | 1826472435132661760 |
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| author | Dey, Nilanjan, 1984-, editor 611303 Borra, Surekha, editor 623368 Ashour, Amira, editor 648477 Shi, Fuqian, editor 648493 ScienceDirect (Online service) 7722 |
| author_facet | Dey, Nilanjan, 1984-, editor 611303 Borra, Surekha, editor 623368 Ashour, Amira, editor 648477 Shi, Fuqian, editor 648493 ScienceDirect (Online service) 7722 |
| author_sort | Dey, Nilanjan, 1984-, editor 611303 |
| collection | OCEAN |
| description | Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers |
| first_indexed | 2024-03-05T17:18:27Z |
| format | software, multimedia |
| id | KOHA-OAI-TEST:605850 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T17:18:27Z |
| publishDate | 2019 |
| publisher | London : Academic Press, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:6058502023-10-18T06:23:22ZMachine Learning in Bio-Signal Analysis and Diagnostic Imaging / Dey, Nilanjan, 1984-, editor 611303 Borra, Surekha, editor 623368 Ashour, Amira, editor 648477 Shi, Fuqian, editor 648493 ScienceDirect (Online service) 7722 software, multimedia Electronic books 631902 London : Academic Press,©20192019engMachine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineersChapter 1. Ontology-Based Process for Unstructured Medical Report Mapping -- Chapter 2. A Computer-Aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images -- Chapter 3. A DEFS Based System for Differential Diagnosis Between Severe Fatty Liver and Cirrhotic Liver Using Ultrasound Images -- Chapter 4. Infrared Thermography and Soft Computing for Diabetic Foot Assessment -- Chapter 5. Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN, and SVM Classifiers Using HRV Analysis -- Chapter 6. Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization Using Digitized Screen Film Mammograms -- Chapter 7. Optimization of ANN Architecture: A Review on Nature-Inspired Techniques -- Chapter 8. Ensemble Learning Approach to Motor Imagery EEG Signal Classification -- Chapter 9. Medical Images Analysis Based on Multilabel Classification -- Chapter 10. Figure Retrieval From Biomedical Literature: An Overview of Techniques, Tools, and Challenges -- Chapter 11. Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images -- Chapter 12. Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective -- Index.Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineersBiomedical engineeringSignal processinghttps://www.sciencedirect.com/science/book/9780128160862URN:ISBN:9780128160862Remote access restricted to users with a valid UTM ID via VPN. |
| spellingShingle | Biomedical engineering Signal processing Dey, Nilanjan, 1984-, editor 611303 Borra, Surekha, editor 623368 Ashour, Amira, editor 648477 Shi, Fuqian, editor 648493 ScienceDirect (Online service) 7722 Machine Learning in Bio-Signal Analysis and Diagnostic Imaging / |
| title | Machine Learning in Bio-Signal Analysis and Diagnostic Imaging / |
| title_full | Machine Learning in Bio-Signal Analysis and Diagnostic Imaging / |
| title_fullStr | Machine Learning in Bio-Signal Analysis and Diagnostic Imaging / |
| title_full_unstemmed | Machine Learning in Bio-Signal Analysis and Diagnostic Imaging / |
| title_short | Machine Learning in Bio-Signal Analysis and Diagnostic Imaging / |
| title_sort | machine learning in bio signal analysis and diagnostic imaging |
| topic | Biomedical engineering Signal processing |
| url | https://www.sciencedirect.com/science/book/9780128160862 |
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