Bøker i Primers in Biomedical Imaging Devices and Systems-serien

Biomedical Sensors and Smart Sensing: A Beginner''s Guide, a book in the 10-volume Primers in Biomedical Imaging Devices and Systems series, covers a wide range of interdisciplinary applications in imaging modalities, nuclear medicine, computed tomographic systems, x-ray systems, magnetic resonance imaging, ultrasound, and virtual reality. The series explores the essential fundamental techniques required to analyze and process signals and images for diagnosis, scientific discovery and medical applications. Volumes in this series cover a wide range of interdisciplinary areas, combining foundational content with practical case studies to demonstrate the applications of these technologies in real-world situations. In addition, the 10-volume series considers various medical devices, electronics, circuits, sensors and algorithms. Several applications ranging from basic biological science to clinical practice are included to facilitate ongoing research. Covers a variety of sensing and signal processing techniques Introduces different approaches relating to communication and intelligent data processing for early detection and prediction of diseases Includes practical case studies

Magnetic Resonance Imaging: Recording, Reconstruction and Assessment gives a detailed overview of magnetic resonance imaging (MRI), along with its applications and challenges. The book explores the abnormalities in internal human organs using MRI techniques while also featuring case studies that illustrate measures used. In addition, it explores precautionary measures used during MRI based imaging, the selection of appropriate contrast agents, and the selection of the appropriate modality during the image registration. Sections introduce medical imaging, the use of MRI in brain, cardiac, lung and kidney detection, and also discuss both 2D and 3D imaging techniques and various MRI modalities. This volume will be of interest to researchers, engineers and medical professionals involved in the development and use of MRI systems. Discusses challenges and issues faced, as well as safety precautions to be followed Features case studies with benchmark MRIs existing in the literature Introduces computer-based assessment (Machine Learning and Deep Learning) of the MRI based on its 2D slices

Applied Speech Processing: Algorithms and Case Studies is concerned with supporting and enhancing the utilization of speech analytics in several systems and real-world activities, including sharing data analytics related information, creating collaboration networks between several participants, and the use of video-conferencing in different application areas. The book provides a well-standing forum to discuss the characteristics of the intelligent speech signal processing systems in different domains. The book is proposed for professionals, scientists, and engineers who are involved in new techniques of intelligent speech signal processing methods and systems. It provides an outstanding foundation for undergraduate and post-graduate students as well.Includes basics of speech data analysis and management tools with several applications, highlighting recording systemsCovers different techniques of big data and Internet-of-Things in speech signal processing, including machine learning and data miningOffers a multidisciplinary view of current and future challenges in this field, with extensive case studies on the design, implementation, development and management of intelligent systems, neural networks, and related machine learning techniques for speech signal processing

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ''with'' and ''without'' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. Provides a step-by-step approach to develop deep learning models Presents case studies showing end-to-end implementation (source codes: available upon request)