Revisiting N-CNN for Clinical Practice
Leonardo Antunes Ferreira Lucas Pereira Carlini Gabriel de Almeida Sá Coutrin Tatiany Marcondes Heideirich Marina Carvalho de Moraes Barros Ruth Guinsburg Carlos Eduardo Thomaz
This paper revisits the Neonatal Convolutional Neural Network (N-CNN) by
optimizing its hyperparameters and evaluating how they affect its
classification metrics, explainability and reliability, discussing their
potential impact in clinical practice. We have chosen hyperparameters that do
not modify the original N-CNN architecture, but mainly modify its learning rate
and training regularization. The optimization was done by evaluating the
improvement in F1 Score for each hyperparameter individually, and the best
hyperparameters were chosen to create a Tuned N-CNN. We also applied soft
labels derived from the Neonatal Facial Coding System, proposing a novel
approach for training facial expression classification models for neonatal pain
assessment. Interestingly, while the Tuned N-CNN results point towards
improvements in classification metrics and explainability, these improvements
did not directly translate to calibration performance. We believe that such
insights might have the potential to contribute to the development of more
reliable pain evaluation tools for newborns, aiding healthcare professionals in
delivering appropriate interventions and improving patient outcomes.