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The field of healthcare is seeing a rapid expansion of
technological advancement within current medical practices. The
implementation of technologies including neural networks,
multi-model imaging, genetic algorithms, and soft computing are
assisting in predicting and identifying diseases, diagnosing
cancer, and the examination of cells. Implementing these biomedical
technologies remains a challenge for hospitals worldwide, creating
a need for research on the specific applications of these
computational techniques. Deep Neural Networks for Multimodal
Imaging and Biomedical Applications provides research exploring the
theoretical and practical aspects of emerging data computing
methods and imaging techniques within healthcare and biomedicine.
The publication provides a complete set of information in a single
module starting from developing deep neural networks to predicting
disease by employing multi-modal imaging. Featuring coverage on a
broad range of topics such as prediction models, edge computing,
and quantitative measurements, this book is ideally designed for
researchers, academicians, physicians, IT consultants, medical
software developers, practitioners, policymakers, scholars, and
students seeking current research on biomedical advancements and
developing computational methods in healthcare.
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