Neonatal Jaundice Detection System

• Published in: Journal of medical systems 2016-07, Vol.40 (7), p.166-166, Article 166
• Main Authors: Aydın, Mustafa, Hardalaç, Fırat, Ural, Berkan, Karap, Serhat
• Format: Article
• Language: English
• Subjects: Babies; Bilirubin; Blood; Calibration; Color; Early Diagnosis; Feature extraction; Health Informatics; Health Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Infant, Newborn; Infants; Jaundice; Jaundice, Neonatal - diagnosis; Jaundice, Neonatal - diagnostic imaging; Learning algorithms; Machine learning; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Neonatal care; Neonates; Photography - instrumentation; Photography - methods; Physicians; Pixels; Segmentation; Skin Pigmentation; Smart and Interactive Healthcare Systems; Smartphone; Statistics for Life Sciences; Studies; Transactional Processing Systems; White balancing
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-016-0523-4

Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels’ important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %.