Diagnostic Biomedical Signal And Image Processing Applications With Deep Learning Methods
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Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
Analysis of medical signals and images plays an important role in the early diagnosis and treatment of diseases. Thanks to the development of technology, widespread use of medical imaging devices has become beneficial to human health. However, despite advances in technology, the number of patients and the workload of healthcare professionals is ever increasing. Deep learning methods and approaches have the potential to help relieve the workload of doctors and facilitate early diagnosis. This can help to improve the healthcare system and people's quality of life. Diagnostic Biomedical Signal and Image Processing Applications presents comprehensive research focusing on both medical imaging and medical signals analysis. It discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, among others. These image and signal modalities include real challenges, which are the main themes that medical imaging and medical signal processing researchers focus on today. The proposed book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers. Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders Explores implementation of novel deep learning and CNN methodologies and their impact studies tested on different medical case studies Presents end-to-end CNN architectures for automatic detection of situations where early diagnosis is important Includes novel methodologies, datasets, design and simulation examples
Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods
Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases. - Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders - Explores the implementation of novel deep learning and CNN methodologies and their impact studies that have been tested on different medical case studies - Presents end-to-end CNN architectures for automatic detection of situations where early diagnosis is important - Includes novel methodologies, datasets, design and simulation examples
Biomedical Signal Processing for Healthcare Applications
This book examines the use of biomedical signal processing—EEG, EMG, and ECG—in analyzing and diagnosing various medical conditions, particularly diseases related to the heart and brain. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals greatly benefits the healthcare sector by improving patient outcomes through early, reliable detection. The discussion of these modalities promotes better understanding, analysis, and application of biomedical signal processing for specific diseases. The major highlights of Biomedical Signal Processing for Healthcare Applications include biomedical signals, acquisition of signals, pre-processing and analysis, post-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases. Emphasis is given to brain and heart signals because incomplete interpretations are made by physicians of these aspects in several situations, and these partial interpretations lead to major complications. FEATURES Examines modeling and acquisition of biomedical signals of different disorders Discusses CAD-based analysis of diagnosis useful for healthcare Includes all important modalities of biomedical signals, such as EEG, EMG, MEG, ECG, and PCG Includes case studies and research directions, including novel approaches used in advanced healthcare systems This book can be used by a wide range of users, including students, research scholars, faculty, and practitioners in the field of biomedical engineering and medical image analysis and diagnosis.