Classification of Facial Expression of Post-Surgical Pain in Children

Abstract

There are certain difficulties in differentiating between children's facial expression related to pain and other stimuli. In addition, the limited communication ability of children in the preverbal stage leads to misdiagnosis when the child feels pain, for example, post-surgical conditions. In this article, a classification approach of facial expression of child pain is presented based on models of pre-trained convolutional neuronal networks from the study carried out in a Colombian hospital of level 4 (Hospital Universitario San Vicente Fundación), in the recovery areas of child surgery services. AlexNet and VGG (16, 19 and Face) networks are evaluated in the own dataset using the FLACC scale and their performances are compared in three experiments. The results show that the VGG-19 model achieves the best performance (92.9%) compared to the other networks. The effectiveness of the model and transfer learning for the classification of facial expression of child pain shows a promising solution for the assessment of post-surgical pain.