Deep Learning Algorithms for Internet of Medical Things

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Deep Learning Algorithms for Internet of Medical Things.

Traditionally, devices used in medical industry predominantly rely on medical images and sensor data; this medical data is processed to study the patient’s health condition and information. Presently, the medical industry requires more innovative technologies to process the large volume of data and improve the quality of service in patient care, and needs an intelligent system to detect early symptoms of diseases in the beginning stage and provide appropriate treatment.

Internet of Medical Things (IoMT) and its recent advancements have included a new dimension towards enhancing the medical industry practices and realizing an intelligent system. In addition, the medical data of IoMT systems is constantly growing because of increasing peripherals introduced in patient care.

Conventionally, medical image processing and machine learning are used for any medical diagnosis, subsequent treatment and therapies. However, with increasing volume of data with increased dimensions and dynamics of medical data, machine learning takes a back seat over another powerful classification mechanism named deep learning. Deep learning can solve more complicated problems, unsolvable by machine learning, and produce highly accurate diagnoses. The medical industry is one of the biggest industries which implements deep learning algorithms. Deep learning can handle the large volume of medical data, including medical reports, patients’ records, and insurance records, helping medical experts to predict the necessary treatment. The scalability of deep learning which helps to process and manipulate this huge volume of data makes an indomitable paradigm for computer-aided diagnosis in medical informatics. The significance of deep learning is compounded by the ever-improving technological aspects towards acquiring precise and multidimensional IoMT data with an eye on improving the accuracy of diagnosis. Overall, incorporating deep learning into IoMT can provide radical innovations in medical image processing, disease diagnosing, medical big data analysis and pathbreaking medical applications.

This Special Section in IEEE Access provides a perfect platform to discuss the prospective developments and innovative ideas in the healthcare domain, with the inception of deep learning-based biomedical systems in IoMT.

The topics of interest include, but are not limited to:

  • Deep learning for energy management in IoMT devices
  • Deep learning algorithms for medical big data analysis
  • Advancements in deep learning algorithms in health informatics
  • Programmers perspective view on deep learning in IoMT
  • Cognitive deep learning for wearable medical devices
  • Deep learning for data analytics in body sensor networks
  • Monitoring wearable medical devices using deep learning
  • Cyber security and malware detection in IoMT using deep learning
  • Deep neural networks for diagnosing and identifying suspicious lesions in IoMT
  • Evidence-based treatment using IoMT
  • Fraud prevention in healthcare using IoMT based data analytics model
  • Telemedicine using IoMT
  • Patient-centric model using deep learning with IoMT
  • Abnormal identification in patient monitoring system using deep learning with IoMT
  • Advanced medical image processing using deep learning
  • Disease diagnosis using deep learning in IoMT
  • Prioritized medical data transmission in IoMT using deep learning
  • Deep Belief Neural network and IoMT in in Future Medical Industries
  • Deep learning-based privacy preserving and security methods for medical data transmission in IoMT
  • Deep learning algorithm for medical decision support systems in IoMT based big data

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor:  Wei Wei, Xi’an University of Technology, China

Guest Editors:

  1. Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil
  2. Naveen Chilamkurti, La Trobe University, Australia
  3. Marcin Woźniak, Silesian University of Technology, Poland
  4. Wael Guibene, Amazon Lab126, USA


Relevant IEEE Access Special Sections:

  1. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things
  2. Data Mining for Internet of Things
  3. Deep Learning for Computer-aided Medical Diagnosis

IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact:;