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Open AccessArticle

Deep-Learning-Based Models for Pain Recognition: A Systematic Review

College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
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Appl. Sci. 2020, 10(17), 5984; https://doi.org/10.3390/app10175984
Received: 31 July 2020 / Revised: 25 August 2020 / Accepted: 27 August 2020 / Published: 29 August 2020
(This article belongs to the Special Issue Data Science for Healthcare Intelligence)
Traditional standards employed for pain assessment have many limitations. One such limitation is reliability linked to 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. Furthermore, it presents the major deep-learning methods used in the review papers. Finally, it provides a discussion of the challenges and open issues. View Full-Text
Keywords: pain assessment; pain recognition; deep learning; neural network; review; dataset pain assessment; pain recognition; deep learning; neural network; review; dataset
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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.

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