M. Al-Eidan, R.; Al-Khalifa, H.; Al-Salman, A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Appl. Sci.2020, 10, 5984.
M. Al-Eidan, R.; Al-Khalifa, H.; Al-Salman, A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Appl. Sci. 2020, 10, 5984.
M. Al-Eidan, R.; Al-Khalifa, H.; Al-Salman, A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Appl. Sci.2020, 10, 5984.
M. Al-Eidan, R.; Al-Khalifa, H.; Al-Salman, A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Appl. Sci. 2020, 10, 5984.
Abstract
The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.
Keywords
pain assessment; pain recognition; deep learning; neural network; dataset
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
