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Deep Learning and the Future of Biomedical Image Analysis

  • Monika JyotiyanaEmail author
  • Nishtha Kesswani
Chapter
Part of the Studies in Big Data book series (SBD, volume 68)

Abstract

Deep Learning (DL) is popular among the researchers and academicians due to its reliability and accuracy, especially in the field of engineering and medical sciences. In the field of medical imaging for the diagnosis of disease, DL techniques are very helpful for early detection. Most important features of DL techniques are that they are uncomplicated with lower complexity, which ultimately saves the time and money and tackle many tough tasks simultaneously. Artificial Intelligence (AI) and Deep Learning (DL) technologies have rapidly improved in recent years. These techniques played an important role in every field of application, especially in the medical field such as in image processing, image fusion, image segmentation, image retrieval, image analysis, computer aided diagnosis (CAD), image registration and, image-guided therapy and many more. The aim of writing this chapter is to describe the DL methods and, the future of biomedical imaging using DL in detail and discuss the issues and challenges.

Keywords

Machine Learning Deep Learning Convolutional Neural Networks Recurrent Neural Network Computer-Aided Diagnosis 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Central University of RajasthanBandar Sindri, AjmerIndia

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