Classification of neonatal jaundice in mobile application with noninvasive image processing methods

This study aims a mobile support system to aid health care professionals in hospitals or in regions far away from hospitals to utilize noninvasive image processing methods for classification of neonatal jaundice. A considerably low processing cost is aimed to be attained by developing an algorithm that could work on a mobile device with low-end camera and processor capabilities within this study. In this context, an algorithm with low cost is developed performing detection of most meaningful parameters by a multiple input single output regression model and correlation.The advantage of the proposed method is that it can estimate bilirubin with the help of a simple regression curve. The reason for its low cost is that the noninvasive jaundice prediction is performed with a simple regression curve instead of many mathematical operations in morphological image processing methods. The study was performed on a total of 196 subjects, 61 of which were classified as severe jaundice while 95 of the newborns were mild jaundice cases, and other 40 cases are used for tests. As a result of this work, the two-group classification accuracy of the developed algorithm is observed to be 92.5% for the 40 subject test group.

Automated summary

This study developed a low-cost mobile support system to assist healthcare professionals in classifying neonatal jaundice using noninvasive image processing methods and estimating bilirubin levels through a simple regression curve. The algorithm achieved a classification accuracy of 92.5% in a test group of 40 subjects, showing promising results for accurate jaundice classification.