Information Technology Department, Faculty of Computers and Information, Menoufia University,
School of Computer Science and Technology, Harbin Institute of Technology,
Harbin, Hei Longjiang, China
Data Science Group, University of Paderborn,
Department of Electrical Engineering and Information Systems, University of Pannonia,
DeustoTech, Deusto University,
Computational Mathematics Department, Faculty of Science, Menoufia University,
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
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Please download to know all details of the Special Issue
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:
Machine learning/deep learning for biomedical imaging
Biomedical imaging and pattern recognition
Biomedical Signal and Image Processing of IoT
Biomedical data mining, data modelling and big-data analytics
IoT architecture, implementation and medical application using edge computing
New Edge computing architecture for biomedical imaging