Real Time Diagnosis of Neonatal Jaundice using Machine Learning

Simple item page
dc.contributor.authorAkarapanuvitaya, Napat
dc.contributor.authorPintavirooj, Chuchart
dc.date.accessioned2026-08-06T10:49:16Z
dc.date.available2026-08-06T10:49:16Z
dc.date.issued2025-01-01
dc.description.abstractNeonatal jaundice, a condition commonly found among newborns, usually requires invasive and timely methods for diagnosis to prevent further complications. This research presents a real-time diagnostic system for neonatal jaundice using machine learning and image processing techniques. The system utilizes a dataset of neonatal images, which undergo preprocessing to extract relevant features. Features, including color values from different color spaces, are analyzed using multiple machine learning models, such as XGBoost, CatBoost, Support Vector Machines (SVM), Random Forest (RF), and LightGBM. These models are trained and evaluated for their predictive performance. A user-friendly graphical user interface is developed to enable real-time diagnosis, implemented on a Raspberry Pi device equipped with a webcam to acquire real-time image capture and apply image processing. The system demonstrates the potential for accessible and reliable neonatal care solutions.
dc.identifier.citationBmeicon 2025 17th Biomedical Engineering International Conference, 2025
dc.identifier.doi10.1109/BMEICON66226.2025.11113689
dc.identifier.other2-s2.0-105015547187
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16473
dc.sourceBmeicon 2025 17th Biomedical Engineering International Conference
dc.subjectImage Processing
dc.subjectMachine Learning Models
dc.subjectNeonatal Jaundice
dc.subjectRaspberry Pi. (key words)
dc.subjectSkin Detection
dc.titleReal Time Diagnosis of Neonatal Jaundice using Machine Learning
dc.typeConference Paper

Files

Collections

Research