Full Description
This book on classification in biomedical image applications presents original and valuable research work on advances in this field, which covers the taxonomy of both supervised and unsupervised models, standards, algorithms, applications and challenges.
Further, the book highlights recent scientific research on artificial neural networks in biomedical applications, addressing the fundamentals of artificial neural networks, support vector machines and other advanced classifiers, as well as their design and optimization.
In addition to exploring recent endeavours in the multidisciplinary domain of sensors, the book introduces readers to basic definitions and features, signal filters and processing, biomedical sensors and automation of biomeasurement systems. The target audience includes researchers and students at engineering and medical schools, researchers and engineers in the biomedical industry, medical doctors and healthcare professionals.
Contents
Introduction to Medical Imaging and Objective Quality Assessment Techniques.- A Novel Approach for the Classification of Liver MR Images Using Complex Orthogonal Ripplet-II and Wavelet-Based Transforms.- ECG based Myocardial Infarction Detection using Hybrid Classification Techniques.- Machine Learning based Optical Plant Identification, Disease Detection and Disease Severity Assessment: State-of-the-Art .- Crop Disease Protection Using Parallel Machine Learning Approaches.- Deep Learning for Medical Image Processing: Overview, Challenges and Future.- On the Fly Segmentation of Intravascular Ultrasound Images Powered by Learning of Backscattering Physics.- ECG signal Dimensionality Reduction based Atrial Fibrillation Detection .- A Bio Application for Accident Victims' Identification using Biometrics.
