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


Hardalaç F., Aydın M., Kutbay U., Ayturan K., Akyel A., Caglar A., ...More

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.29, no.4, pp.2116-2126, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.3906/elk-2008-76
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.2116-2126
  • Keywords: Neonatal&nbsp, jaundice&nbsp, Indirect hyperbilirubinemia, multiple&nbsp, regression analysis&nbsp, image interpretation, BILIRUBIN
  • Gazi University Affiliated: Yes

Abstract

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.