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A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2018, 23(12), pp.131-136
  • DOI : 10.9708/jksci.2018.23.12.131
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 20, 2018
  • Accepted : November 12, 2018
  • Published : December 31, 2018

Jin-Gyo Jeong 1 Lee Myungsuk 1

1계명대학교

Accredited

ABSTRACT

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

Citation status

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