Adel Elmaghraby

Director of Industry Research & Innovation

Winnia Professor of Computer Science & Engineering

Adel S. Elmaghraby, an IEEE Life Senior Member, is professor and former chairman of the Computer Engineering and Computer Science Department at the University of Louisville. He has also held appointments at Carnegie Mellon's Software Engineering Institute and the University of Wisconsin-Madison, and has advised over 60 master's graduates and 24 doctoral graduates. His research and publications span intelligent systems, neural networks, cyber-security, visualization and simulation. The IEEE-Computer Society has recognized his work with multiple awards including a Golden Core membership.


  • Ph.D. in Electrical Engineering, University of Wisconsin - Madison, 1982
  • M.S. in Electrical Engineering, University of Wisconsin - Madison, 1978
  • B.S. in Computer Science, Alexandria University, 1973


Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods- 2020

Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The characteristics of these wounds are crucial indicators for the progress of the healing. While invasive methods to retrieve information are not only painful to the patients but may also increase the risk of infections, non-invasive techniques by means of imaging systems provide a better monitoring of the wound healing processes without causing any harm to the patients. These systems should include an accurate segmentation of the wound, the classification of its tissue types, the metrics including the diameter, area and volume, as well as the healing evaluation. Therefore, the aim of this survey is to provide the reader with an overview of imaging techniques for the analysis and monitoring of pressure injuries as an aid to their diagnosis, and proof of the efficiency of Deep Learning to overcome this problem and even outperform the previous methods. In this paper, 114 out of 199 papers retrieved from 8 databases have been analyzed, including also contributions on chronic wounds and skin lesions.

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