기계학습 기반의 공막 색을 통한 황달 컴퓨터 보조진단 알고리즘 개발
A Study on Jaundice Computer-aided Diagnosis Algorithm using Scleral Color based Machine Learning
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (13)
- Stoll BJ, "Nelson Textbook of Pediatrics", pp.592~599 (2004), WB Saunders Co
- American Academy of Pediatrics, "Subcommittee on Hyperbilirubinemia Management of hyperbilirubinemia in the newborn infant 35 or more weeks of gestation", Pediatrics, pp.297~316 (2004)
- 이상락, "신생아황달", Clinical and Experimental Pediatrics, 49(1), pp.6~13 (2006), 대한소아청소년과학회
- Hsahn, "Textbook of pediatrics", pp.343~ (2004), Daehan Printing & Publishing Co
- Maisels MJ, "Neonatology", pp.484~ (1981), JB Lippincott
- Horiguchi T, "Ethnic differences in neonatal jaundice; comparison of Japanese and Caucasian newborn infants", Am J Obstet Gynecol, pp.71~74 (1975)
- Maisels MJ, "Avery's Neonatology;pathophysiology & management of the newborn", pp.768~846 (2005), Lippincott Williams & Wilkins
- Charalambos N., "A comparison between transcutaneous and total serum bilirubin in healthy-term greek neonates with clinical jaundice", Prague Med Rep, 115(1-2), pp.33~42 (2014)
- Lilian de Greef, "BiliCam:Using Mobile Phones to Monitor Newborn Jaundice", Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Compution (2014), ACM Press
- E. Alpaydin, "Introduction to Machine Learning" (2004), MIT Press
- G. Kitagawa, "The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother", Ann. Inst. Stat. Math., 46, pp.605~623 (1994)
- Lei Xu, "Randomized Hough transform (RHT):basic mechanisms, algorithms, and computational complexities", CVGIP: Image Understanding, 57(2), pp.131~154 (1993)
- Alex Mariakakis, "BiliScreen:Smartphone-Based Scleral Jaundice Monitoring for Liver and Pancreatic Disorders", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(2), pp.1~26 (2017)
