Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning

  • Jeong, Jin-Gyo (Undergraduate, Department of Biomedical Engineering, Keimyung University) ;
  • Lee, Myung-Suk (Tabula Rasa College, Keimyung University)
  • Received : 2018.09.20
  • Accepted : 2018.11.12
  • Published : 2018.12.31
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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.

Keywords

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Fig. 1. Machine Learning Procedures

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Fig. 2. Eye Segmentation Algorithm

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Fig. 3 Eye estimation result

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Fig. 5. Iris segmentation & Sclera segmentation algorithm

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Fig. 7. SVM classification result of jaundice diagnosis

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Fig. 8. Diagnosis of jaundice system GUI

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Fig. 4. (a) imfill function (b) bwareaopen function (c) imopen function

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Fig. 6. (a) Hough transform result (b) Sclera segmentation result

Table 1. Variations for feature extraction[13]

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