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.
@article{arxiv.2308.05877,
title = {Revisiting N-CNN for Clinical Practice},
author = {Leonardo Antunes Ferreira and Lucas Pereira Carlini and Gabriel de Almeida Sá Coutrin and Tatiany Marcondes Heideirich and Marina Carvalho de Moraes Barros and Ruth Guinsburg and Carlos Eduardo Thomaz},
journal= {arXiv preprint arXiv:2308.05877},
year = {2023}
}