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Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
A comprehensive guide on bio-signal analysis and diagnostic imaging for biomedical engineers, radiologists, neurologists and researchers in signal analysis
Nilanjan Dey (Edited by), Surekha Borra (Edited by), Amira S. Ashour (Edited by), Fuqian Shi (Edited by)
9780128160862
Paperback, published 5 December 2018
345 pages
23.4 x 19 x 2.2 cm, 0.72 kg
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.
1. Ontology-based Process for Unstructured Medical Report Mapping 2. A Computer-aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images 3. A DEFS based System for Differential Diagnosis between Severe Fatty Liver and Cirrhotic Liver using Ultrasound Images 4. Infrared Thermography and Soft Computing for Diabetic Foot Assessment 5. Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN and SVM Classifiers using HRV Analysis 6. Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization using Digitized Screen Film Mammograms 7. Optimization of ANN architecture: A review on nature-inspired techniques 8. Ensemble Learning Approach to Motor-Imagery EEG Signal Classification 9. Medical Images Analysis Based on Multi-Label Classification Methods 10. Figure Search in Biomedical Domain: A Survey of Techniques and Challenges 11. Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images 12. Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective
Subject Areas: Biomedical engineering [MQW]
