Neonatal pain assessment might suffer variation among health professionals, leading to late intervention and flimsy treatment of pain in several occasions. Therefore, it is essential to develop computational tools of pain assessment, less subjective and susceptible to external variable influences. Deep learning models, especially Convolutional Neural Networks, have gained ground in the last decade, due to many successful applications in image analysis, object recognitions and human emotion recognitions. In this context, the general aim this dissertation was analyse quantitatively and qualitatively models of Convolutional Neural Networks in the task neonatal pain classification through a computacional framework based in face images of two distinct databases (an international, named COPE, and other national, named UNIFESP). How specific aims were implemented, evaluated and compared the performance of three existent models used in literature: Neonatal Convolutional Neural Network (N-CNN) and two type of ResNet50 models. The quantitative results showed the excellence of N-CNN to neonatal pain assessment automatic, with average accuracy of 87.2% and 78.7% for the databases COPE and UNIFESP, respectively. However, the quantitative analysis showed that all neural models evaluated, including N-CNN models, can learn artifacts from the imagens and not variation discriminating in faces, thus showed the necessity more studies to apply this models in clinical practice
Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG
