Deep learning in multimedia healthcare applications: a review

Diana P. Tobón, M. Shamim Hossain, Ghulam Muhammad, Josu Bilbao, Abdulmotaleb El Saddik

Research output: Contribution to journalArticlepeer-review

Abstract

The increase in chronic diseases has affected the countries’ health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens’ needs. Deep learning techniques is a technological solution that has been used in healthcare lately. Nowadays, with the increase in chips processing capabilities, increase size of data, and the progress in deep learning research, healthcare applications have been proposed to provide citizens’ health needs. In addition, a big amount of data is generated every day. Development in Internet of Things, gadgets, and phones has allowed the access to multimedia data. Data such as images, video, audio and text are used as input of applications based on deep learning methods to support healthcare system to diagnose, predict, or treat patients. This review pretends to give an overview of proposed healthcare solutions based on deep learning techniques using multimedia data. We show the use of deep learning in healthcare, explain the different types of multimedia data, show some relevant deep learning multimedia applications in healthcare, and highlight some challenges in this research area.

Original languageEnglish
Pages (from-to)1465-1479
Number of pages15
JournalMultimedia Systems
Volume28
Issue number4
DOIs
StateAccepted/In press - 2022

Keywords

  • Chronic disease
  • COVID-19
  • Deep learning
  • Healthcare
  • Monomedia
  • Multimedia
  • Multimodal

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