Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

Leila Ismail, Muhammad Danish Waseem

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

Original languageEnglish
Pages (from-to)339-347
Number of pages9
JournalProcedia Computer Science
Volume220
DOIs
Publication statusPublished - 2023
Event14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 - Leuven, Belgium
Duration: Mar 15 2023Mar 17 2023

Keywords

  • Computer Vision
  • Deep Learning
  • Image Processing
  • Machine Learning
  • Pain Detection
  • Patient-Centric
  • Smart healthcare
  • eHealth

ASJC Scopus subject areas

  • General Computer Science

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