Non-invasive and non-contact automatic jaundice detection of infants based on random forest

Article

Khanam, Fatema-Tuz-Zohra, Al-Naj, Ali, Perera, Asanka G., Wang, Danyi and Chahl, Javaan. 2023. "Non-invasive and non-contact automatic jaundice detection of infants based on random forest." Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization. 11 (6), pp. 2516-2529. https://doi.org/10.1080/21681163.2023.2244601
Article Title

Non-invasive and non-contact automatic jaundice detection of infants based on random forest

ERA Journal ID212144
Article CategoryArticle
AuthorsKhanam, Fatema-Tuz-Zohra, Al-Naj, Ali, Perera, Asanka G., Wang, Danyi and Chahl, Javaan
Journal TitleComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Journal Citation11 (6), pp. 2516-2529
Number of Pages14
Year2023
PublisherTaylor & Francis
Place of PublicationUnited Kingdom
ISSN2168-1163
2168-1171
Digital Object Identifier (DOI)https://doi.org/10.1080/21681163.2023.2244601
Web Address (URL)https://www.tandfonline.com/doi/full/10.1080/21681163.2023.2244601
Abstract

Jaundice is a common phenomenon in neonates and a significant cause of morbidity and mortality. Early detection of jaundice is still a significant challenge. Moreover, existing invasive techniques are stressful, and painful for the newborn, and non-invasive devices are expensive. Therefore, we investigate the characteristics of a non-invasive and non-contact neonatal jaundice detection system based on skin colour analysis and machine learning using a graphical user interface (GUI). First, we automatically selected a region of interest (ROI) from the image of an infant captured by a digital camera. Then, the skin colour of the selected ROI was analysed in both RGB and YCbCr colour spaces. Finally, a machine learning algorithm based on random forest (RF) was incorporated to classify jaundice or normal and determine whether the neonate requires treatment or not. The experimental result demonstrates that the proposed jaundice detection system has the potential as a non-invasive technique in clinical application.

Keywordsneonatal jaundice; bilirubin; automatic ROI selection; kincolour analysis; machinelearning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204603. Computer vision and multimedia computation
Byline AffiliationsUniversity of South Australia
Middle East Technical University, Turkey
University of New South Wales
Defence Science and Technology Group, Australia
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