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.- Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging- Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining- Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains
Table of Contents
1. Ontology-based Process for Unstructured Medical Report Mapping2. A Computer-aided Diagnoses System for Detecting Multiple Ocular Diseases Using Color Retinal Fundus Images3. A DEFS based System for Differential Diagnosis between Severe Fatty Liver and Cirrhotic Liver using Ultrasound Images4. Infrared Thermography and Soft Computing for Diabetic Foot Assessment5. Automated Classification of Hypertension and Coronary Artery Disease Patients by PNN, KNN and SVM Classifiers using HRV Analysis6. Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization using Digitized Screen Film Mammograms7. Optimization of ANN architecture: A review on nature-inspired techniques8. Ensemble Learning Approach to Motor-Imagery EEG Signal Classification9. Medical Images Analysis Based on Multi-Label Classification Methods10. Figure Search in Biomedical Domain: A Survey of Techniques and Challenges11. Application of Machine Learning Algorithms for Classification and Security of Diagnostic Images12. Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective
