Year: 2025
Type: conference-paper
Abstract: Neonatal hyperbilirubinemia, marked by elevated bilirubin levels, can lead to jaundice and severe complications if untreated. Conventional diagnosis involves invasive blood sampling, which is time-intensive and stressful for newborns. This study presents a non-invasive bilirubin estimation method using image processing, offering a faster, more accessible alternative. Key steps include skin detection, region of interest (ROI) extraction, and conversion to the YCbCr color space to enhance sensitivity to bilirubin-induced color shifts, especially in the Cb channel. Machine learning algorithms: K-Nearest Neighbors (k-NN), Random Forest (RF), and XGBoost, were evaluated, with XGBoost achieving 98% accuracy and the lowest mean squared error (MSE). This approach enables rapid jaundice detection, reducing reliance on repeated blood tests. Its integration into clinical practices and home-based monitoring systems offers the potential for early diagnosis and timely intervention, significantly improving neonatal healthcare outcomes.
Source: AIJR Proceedings
Authors: Lakshmi A Menon, Mary Ansteena Joseph, Navya N A, Afsalu Rahman S, Surya D
Institution: APJ Abdul Kalam Technological University
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4415247129