Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Annamalai Suresh (Anna University, India), R. Udendhran (Bharathidasan University, India) and S. Vimal (Anna University, India)
Projected Release Date: June, 2020|Copyright: © 2020 |Pages: 300
ISBN13: 9781799835912|ISBN10: 179983591X|EISBN13: 9781799835929|DOI: 10.4018/978-1-7998-3591-2


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.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Biomedical Imaging
  • Convolutional Networks
  • Data Collection Methods
  • Edge Computing
  • Genetic Data Analysis
  • Machine Learning
  • Practical Healthcare Systems
  • Prediction Models
  • Quantitative Measurements
  • Visualization

Table of Contents and List of Contributors

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