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

arXiv: https://arxiv.org/abs/2303.08273 · HTML: https://mlsanity.com/paper/2303.08273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition](https://mlsanity.com/paper/2104.07389/) (2021)

## Built on by (most cited first, 2 of 2)

- [ChatGPT, Let us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions](https://mlsanity.com/paper/2401.06804/) (2024)
- [GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation](https://mlsanity.com/paper/2502.12223/) (2025)

## Abstract (arXiv)

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.