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"Biomedical Imaging: Principles and Advancements" offers a captivating exploration of the intricate landscapes within the human body, revealing the transformative power of biomedical imaging. Edited by Wellington Pinheiro dos Santos, Juliana Carneiro Gomes, Maíra Araújo de Santana, and Clarisse Lins de Lima, this anthology delves into foundational concepts, from acquisition to ethical considerations, paving the way for in-depth examinations of magnetic resonance imaging, infrared thermography, and electrical impedance tomography. The real-world applications covered in Section II, from Alzheimer's diagnosis to Covid-19 assessment, showcase the diverse impact of these imaging techniques on healthcare. A collective effort, this volume inspires continued exploration in the ever-evolving field of biomedical imaging.
Despite success with treatment when diagnosed early, breast cancer is still one of the most fatal forms of cancer for women. Imaging diagnosis is still one of the most efficient ways to detect early breast changes with mammography among the most used techniques. However, there are other techniques that have emerged as alternatives or even complementary tests in the early detection of breast lesions (e.g., breast thermography and electrical impedance tomography). Artificial intelligence can be used to optimize image diagnosis, increasing the reliability of the reports and supporting professionals who do not have enough knowledge or experience to make good diagnoses. Biomedical Computing for B...
This book presents the theoretical basis and applications of biomedical signal analysis and processing. Initially, the nature of the most common biomedical signals, such as electroencephalography, electromyography, electrocardiography and others, is described. The theoretical basis of linear signal processing is summarized, with continuous and discrete representation, linear filters and convolutions, Fourier and Wavelets transforms. Machine learning concepts are also presented, from classic methods to deep neural networks. Finally, several applications in neuroscience are presented and discussed, involving diagnosis and therapy, in addition to other applications. Features: Explains signal pr...
Digital technologies have rapidly changed how we bank, borrow and lend, commute, or order food. The scale of these changes, and the relatively low barriers for individuals to drive such systemic change, have raised great expectations for digital technologies to also impact health and healthcare globally. The COVID-19 pandemic has further exacerbated the need for improved health data from low- and middle-income countries (LMICs), and the expectation for digital technologies to provide solutions.
This book presents the fundamentals of swarm intelligence, from classic algorithms to emerging techniques. It presents comprehensive theoretical foundations and examples using the main Computational Intelligence methods in programming languages such as Python, Java and MATLAB®. Real-world applications are also presented in areas as diverse as Medicine, Biology and industrial applications. The book is organized into two parts. The first part provides an introduction to swarming algorithms and hybrid techniques. In the second part, real world applications of swarm intelligence are presented to illustrate how swarm algorithms can be used in applications of optimization and pattern recognition, reviewing the principal methods and methodologies in swarm intelligence.
The growth of smart healthcare has produced the exponential rise of biomedical data and further leads the new challenges in terms of signal analysis, data storage, data retrieval, and image analysis as well as the effective delivery of the results. Applying Internet of Medical Things and computational intelligence in Healthcare 4.0 benefits to obtain improved faster performance, data administration and a high level of accuracy. This trend supports the development of novel Healthcare 4.0 services that can benefit both the healthcare providers and users to transfer toward demanding and researching modified, practical, and analytical healthcare models. More impending of the powerful combination of IoMT, computational intelligence, and healthcare 4.0 is worth discovering.
This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.
Intelligent Diagnosis of Lung Cancer and Respiratory Diseases presents information about diseases of the respiratory system and the relevant diagnostic imaging techniques. The book focuses on intelligent diagnostic imaging systems. The first section of the book deals with the physiological underpinnings of 3 major diseases that affect the respiratory system: tuberculosis, lung cancer and COVID-19. This section also explains the basic principles of artificial Intelligence that support the diagnosis of these diseases. The next section presents applications of intelligent systems to support the imaging diagnosis of COVID-19 and lung cancer, with emphasis on digital health and telemedicine approaches. Each chapter is organized into a readable format, and is accompanied with detailed bibliographical information for further reading. This book is a reference for everyone seeking to understand how artificial intelligence can provide solutions for diagnostic support systems by processing and analyzing radiological images to improve early diagnosis and, consequently, lung disease prognosis.
This book reports on the latest research and developments in Biomedical Engineering, with a special emphasis on topics of interest and findings achieved in Latin America. This third volume of a 4-volume set covers advances in biomechanical analysis and modeling, neural network based methods for medical diagnosis and therapy, and robots and human-machine interface for rehabilitation. Throughout the book, a special emphasis is given to low-cost technologies and to their development for and applications in clinical settings. Based on the IX Latin American Conference on Biomedical Engineering (CLAIB 2022) and the XXVIII Brazilian Congress on Biomedical Engineering (CBEB 2022), held jointly, and virtually on October 24-28, 2022, from Florianópolis, Brazil, this book provides researchers and professionals in the biomedical engineering field with extensive information on new technologies and current challenges for their clinical applications. .