영상 인식 기반 신속 인플루엔자 자동 판독 기법 개발
Development of Automated Rapid Influenza Diagnostic Test Method Based on Image Recognition
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
To examine different types of influenza diagnostic test kits automatically, automated rapid influenza diagnostic test method based on image recognition is proposed in this paper. First, the proposed methods classify a variety of the rapid influenza diagnostic test kit based on support vector machine that analyzes the kits' feature point. Then, to improve the accuracy of test, the proposed methods match the histogram of both the target image of influenza kit and the input image of influenza kit for minimizing the effect of environment factors, such as lighting and exposure variations. And, to minimize the effect from composition of the hand-helds devices, the proposed methods extract the feature point and match point-by-point between target image of influenza kit and input image of influenza kit. Experimental results of 124 experimental group show that the proposed methods significantly have effectiveness, which shows 90% accuracy in moderate antigen, for the preliminary examination of influenza, and provides the opportunity for taking action against influenza.
Abstract
To examine different types of influenza diagnostic test kits automatically, automated rapid influenza diagnostic test method based on image recognition is proposed in this paper. First, the proposed methods classify a variety of the rapid influenza diagnostic test kit based on support vector machine that analyzes the kits' feature point. Then, to improve the accuracy of test, the proposed methods match the histogram of both the target image of influenza kit and the input image of influenza kit for minimizing the effect of environment factors, such as lighting and exposure variations. And, to minimize the effect from composition of the hand-helds devices, the proposed methods extract the feature point and match point-by-point between target image of influenza kit and input image of influenza kit. Experimental results of 124 experimental group show that the proposed methods significantly have effectiveness, which shows 90% accuracy in moderate antigen, for the preliminary examination of influenza, and provides the opportunity for taking action against influenza.
참고문헌 (10)
- H. J. Lee, "Rapid diagnostic tests for influenza", Public Health Weekly Report, 5(8), pp.138~152 (2012)
- J. Logan, "Real-Time PCR:current technology and application" (2009), Caister Academic Press
- M. M. Baddour, "Steps forwards in diagnosing and controlling influenza" (2016), Intech
- M. McTear, "The conversational interface: talking to smart devices" (2016), Springer International Publishing
- L. Fei-Fei, "A bayesian hierarchical model for learning natural scene catogories", Proc. IEEE Conference on Computer Vision and Pattern Recognition (2005)
- H. Bay, "Surf: speeded up robust features", Proc . European Conference on Computer Vision, pp.404~417 (2006)
- C. Cortes, "Support-vector networks", Machine Learning, 20(3), pp.273~297 (1995)
- P. E. Trahanias, "Color image enhancement through 3-D histogram equalization", Proc. International Conference on Pattern Recognition, pp.545~548 (1992)
- S. -K. Yoo, "Study on the real time medical image processing", J. Biomed. Eng. Res, 8(2), pp.118~122 (1987)
