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Biomedical Imaging Technologies for IoT Devices Using Edge Computing
Submission DeadlineJan. 10, 2020

Submission Guidelines: http://www.sciencepublishinggroup.com/home/submission

Lead Guest Editor
Mohamed Adel Hammad
Information Technology Department, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt
Guest Editors
  • Ibrahim Elgendy
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Hei Longjiang, China
  • Hamada Zahera
    Data Science Group, University of Paderborn, Paderborn, Germany
  • Mostafa Elgendy
    Department of Electrical Engineering and Information Systems, University of Pannonia, Pannonia, Hungary
  • Amgad Mohammed
    DeustoTech, Deusto University, Bilbao, Spain
  • Mahmoud Eissa
    Computational Mathematics Department, Faculty of Science, Menoufia University, Menoufia, Egypt
Introduction
Nowadays, with the considerable growth of the Internet-of-Things (IoT) devices ranging from wearables, smartphones, and virtual reality facilities to internet-connected sensors, the field of medical expects to gain a large benefit. Especially, the biomedical imaging technologies utilize either x-rays (CT scans), sound (ultrasound), magnetism (MRI), radioactive pharmaceuticals (nuclear medicine: SPECT, PET) or light (endoscopy, OCT) to acquire and communicate unprecedented data which used to assess the current condition of an organ or tissue as well as monitor the patient over time for diagnostic and treatment evaluation. However, these devices are still resource-constrained with limited computation power and energy where the collected data becomes increasingly complex and needs to be analyzed quickly. Edge computing is becoming more prominent solution in biomedical imaging technologies to overcome these limitations and introduce more internet of things (IoT) devices for analytics as well as facilitate connectivity, data transfer, and query able local database. As the number of analytics solutions and IoT devices introduced into healthcare networks grows, this special issue aims to explore more advanced ways of handling data to ensure clinicians receive data in a real time.
Aims and Scope:
  1. Machine learning/deep learning for biomedical imaging
  2. Biomedical imaging and pattern recognition
  3. Biomedical Signal and Image Processing of IoT
  4. Biomedical data mining, data modelling and big-data analytics
  5. IoT architecture, implementation and medical application using edge computing
  6. New Edge computing architecture for biomedical imaging
Guidelines for Submission
Manuscripts should be formatted according to the guidelines for authors
(see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=147).

Please download the template to format your manuscript.

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