Abstract
Neonatal jaundice, characterized by elevated bilirubin levels causing yellow discoloration of the skin and eyes in newborns, is a critical condition requiring accurate and timely diagnosis. This study proposes a novel approach using 1D Convolutional Neural Networks (1DCNN) for estimating bilirubin levels from RGB, HSV, LAB, and YCbCr color channels extracted from infant images. Initially, each color channel is treated as a time series input to a 1DCNN model, facilitating bilirubin level prediction through regression analysis. Subsequently, RGB feature maps are combined with those derived from HSV, LAB, and YCbCr channels to enhance prediction performance. The effectiveness of these methods is evaluated based on Root Mean Squared Error (RMSE), R-squared (R(2)), and Mean Absolute Error (MAE). Additionally, the best-performing model is adapted for classification of jaundice status. The results show that the integration of RGB and HSV color spaces yields the best performance, with an RMSE of 1.13 and an R(2) score of 0.91. Moreover, the model achieved an impressive accuracy of 96.87% in classifying jaundice status into three categories. This study provides a promising non-invasive alternative for neonatal jaundice detection, potentially improving early diagnosis and management in clinical settings.