Neonatal Pain Assessment From Facial Expression Using Deep Neural Networks

  • Lucas Buzuti USP
  • Tatiany Heideirich USP
  • Marina Barros USP
  • Ruth Guinsburg USP
  • Carlos Thomaz USP


Currently, neonatal pain assessment varies among health professionals, leading to late intervention and flimsy treatment of pain in several occasions. Therefore, it is essential to understand the deficiencies of the current pattern of pain assessment tools in order to develop new ones, less subjective and susceptible to external variable influences. The aim of this paper is to investigate neonatal pain assessment using two models of Deep Learning: Neonatal Convolutional Neural Network trained end-to-end and ResNet trained using Transfer Learning. We used for training two distinct databases (COPE and Unifesp) and our results showed that the use of multi-racial databases might improve the evaluation of automatic models of neonatal pain assessment.

Palavras-chave: Automated pain recognition, deep learning, facial expression, neonatal pain assessment, pattern recognition


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BUZUTI, Lucas; HEIDEIRICH, Tatiany; BARROS, Marina; GUINSBURG, Ruth; THOMAZ, Carlos. Neonatal Pain Assessment From Facial Expression Using Deep Neural Networks. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 87-92. DOI: