Abstract
Newborn pain is a non-stationary made by babies in reaction to certain circumstances. This infant facial expression can be used to recognize physical or psychology condition of newborn. The goal of this study is to evaluate the performance of illumination levels for infant pain classification. Local Binary Pattern (LBP) features are computed at Fuzzy k-NN classifier. Eight different performance measurements such as Sensitivity, Specificity, Accuracy, Area under Curve (AUC), Cohen's kappa (k), Precession, F-Measure and Time Consumption are performed. Fuzzy k-NN classifier is employed to classify the newborn pain. The outcomes accentuated that the suggested features and classification algorithms can be employed to assist the medical professionals for diagnosing pathological condition of newborn pain.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
