Pain Assessment From Facial Expression: Neonatal Convolutional Neural Network (N-CNN)

The current standard for assessing neonatal pain is discontinuous and suffers from inter-observer variations, which can result in delayed intervention and inconsistent treatment of pain. Therefore, it is critical to address the shortcomings of the current standard and develop continuous and less subjective pain assessment tools. Convolutional Neural Networks have gained much popularity in the last decades due to the wide range of its successful applications in medical image analysis, object recognition, and emotion recognition. In this paper, we propose a Neonatal Convolutional Neural Network, designed and trained end-to-end to detect neonatal pain. We evaluated the proposed network in two data sets of neonates and compared its performance to the performance of ResNet architecture in the same data sets. Our proposed method outperformed ResNet in recognizing neonates’ pain and achieved around 91.00% accuracy. While further research is needed, our preliminary results suggest that the presented network can be used for automatic pain assessment, and possibly similar applications. It also suggests that the automatic recognition of neonatal pain provides a viable and more efficient alternative to the current standard of pain assessment.