Convolutional Neural Networks for Neonatal Pain Assessment

The current standard for assessing neonatal pain is discontinuous and inconsistent because it depends highly on the observers bias. These drawbacks can result in delayed intervention and inconsistent treatment of pain. Convolutional neural networks (CNNs) have gained much popularity in the last decades due to the wide range of its successful applications in medical image analysis, object and emotion recognition. In this paper, we investigated the use of a novel lightweight neonatal convolutional neural network as well as other popular CNN architectures for assessing neonatal pain. We experimented with various image augmentation techniques and evaluated the CNN architectures using two real-world datasets [COPE and neonatal pain assessment dataset (NPAD)] collected from neonates while being hospitalized in the intensive care unit. The experimental results demonstrate the superiority and efficiency of the novel network in assessing neonatal pain. They also suggest that the automatic recognition of neonatal pain using CNN networks is a viable and more efficient alternative to the current assessment standard.