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Cognitive Computation

Erschienen in:

01.08.2016

Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip

verfasst von: Nianyin Zeng, Zidong Wang, Hong Zhang, Weibo Liu, Fuad E. Alsaadi

Erschienen in: Cognitive Computation | Ausgabe 4/2016

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Abstract

Gold immunochromatographic strip (GICS) has become a popular membrane-based diagnostic tool in a variety of settings due to its sensitivity, simplicity and rapidness. This paper aimed to develop a framework of automatic image inspection to further improve the sensitivity as well as the quantitative performance of the GICS systems. As one of the latest methodologies in machine learning, the deep belief network (DBN) is applied, for the first time, to quantitative analysis of GICS images with hope to segment the test and control lines with a high accuracy. It is remarkable that the exploited DBN is capable of simultaneously learning three proposed features including intensity, distance and difference to distinguish the test and control lines from the region of interest that are obtained by preprocessing the GICS images. Several indices are proposed to evaluate the proposed method. The experiment results show the feasibility and effectiveness of the DBN in the sense that it provides a robust image processing methodology for quantitative analysis of GICS.

Vorheriger Artikel A Likelihood-Based Qualitative Flexible Approach with Hesitant Fuzzy Linguistic Information

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Chuang L, Hwang J, Chang H, Chang F, Jong SH. Rapid and simple quantitative measurement of \(\alpha \)-fetoprotein by combining immunochromatographic strip test and artificial neural network image analysis system. Clin Chim Acta. 2004;348:87–93.CrossRefPubMed

Ding D, Wang Z, Shen B, Wei G. Event-triggered consensus control for discrete-time stochastic multi-agent systems: the input-to-state stability in probability. Automatica. 2015;62:284–91.CrossRef

Ding D, Wang Z, Lam J, Shen B. Finite-horizon \(H_{\infty }\) control for discrete time-varying systems with randomly occurring nonlinearities and fading measurements. IEEE Trans Autom Control. 2015;60(9):2488–93.CrossRef

Ding D, Wang Z, Shen B, Dong H. \(H_{\infty }\) state estimation with fading measurements, randomly varying nonlinearities and probabilistic distributed delays. Int J Robust Nonlinear Control. 2015;25(13):2180–95.

Faulstich K, Gruler R, Eberhard M, Haberstroh K. Developing rapid mobile POC systems. Part 1: devices and applications for lateral-flow immunodiagnostics. IVD Technol. 2007;13(6):47–53.

Hamel P, Eck D. Learning features from music audio with deep belief networks. In: 11th international society for music information retrieval conference; 2010. p. 339–44

Hinton G. Training products of experts by minimizing contrastive divergence. Neural Comput. 2002;14(8):1771–800.CrossRefPubMed

Hinton G, Deng L, Yu D, Dahl G, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T, Kingsbury B. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82–97.CrossRef

Hinton G, Salakhutdinov R. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504–7.CrossRefPubMed

10.

Hou N, Dong H, Wang Z, Ren W, Alsaadi FE. Non-fragile state estimation for discrete Markovian jumping neural networks. Neurocomputing. 2016;179:238–45.CrossRef

11.

Huang L, Zhang Y, Xie Ch, Qu J, Huang H, Wang X. Research of reflectance photometer based on optical absorption. Int J Light Electron Opt. 2010;121(19):1725–8.CrossRef

12.

Kaur J, Singh K, Boro R, Thampi K, Raje M, Varshney G. Immunochromatographic dipstick assay format using gold nanoparticles labeled protein-hapten conjugate for the detection of atrazine. Environ Sci Technol. 2007;41(14):5028–36.CrossRefPubMed

13.

Li J, Ouellette A, Giovangrandi L, Cooper D, Ricco A, Kovacs G. Optical scanner for immunoassays with up-converting phosphorescent labels. IEEE Trans Biomed Eng. 2008;55(5):1560–71.CrossRefPubMed

14.

Li D, Wei S, Yang H, Li Y, Deng A. A sensitive immunochromatographic assay using colloidal gold-antibody probe for rapid detection of pharmaceutical indomethacin in water samples. Biosens Bioelectron. 2009;24(7):2277–80.CrossRefPubMed

15.

Li Y, Zeng N, Du M. A novel image methodology for interpretation of gold immunochromatographic strip. J Comput. 2011;6(3):540–7.

16.

Lin C, Wu C, Hsu H, Li K, Lin L. Rapid bio-test strips reader with image processing technology. Optik. 2004;115(8):363–9.CrossRef

17.

Liu Y, Alsaadi FE, Yin X, Wang Y. Robust \(H_{\infty }\) filtering for discrete nonlinear delayed stochastic systems with missing measurements and randomly occurring nonlinearities. Int J Gen Syst. 2015;44(2):169–81.CrossRef

18.

Luo Y, Wei G, Liu Y, Ding X. Reliable \(H_{\infty }\) state estimation for 2-D discrete systems with infinite distributed delays and incomplete observations. Int J Gen Syst. 2015;44(2):155–68.CrossRef

19.

Mohamed A, Dahl G, Hinton G. Acoustic modeling using deep belief networks. IEEE Trans Audio Speech Lang Process. 2012;20(1):14–22.CrossRef

20.

Mohamed A, Sainath T, Dahl G, Ramabhadran B, Hinton G, Picheny M. Deep belief networks using discriminative features for phone recognition. In: 2011 IEEE international conference on acoustics, speech and signal processing; 2011. p. 5060–63

21.

Posthuma-Trumpie GA, Korf J, van Amerongen A. Lateral flow (immuno)assay: its strengths, weaknesses, opportunities and threats. A literature survey. Anal Bioanal Chem. 2009;393(2):569–82.CrossRefPubMed

22.

Qian S, Haim H. A mathematical model of lateral flow bioreactions applied to sandwich assays. Anal Biochem. 2003;322(1):89–98.CrossRefPubMed

23.

Qian S, Haim H. Analysis of lateral flow biodetectors: competitive format. Anal Biochem. 2004;326(2):211–24.CrossRefPubMed

24.

Raphael C, Harley Y. Lateral flow immunoassay. New York City: Humana Press; 2008.

25.

Srivastava R, Cheng J, Wong D, Liu J. Using deep learning for robustness to parapapillary atrophy in optic disc segmentation. In: 2015 IEEE 12th international symposium on biomedical imaging; 2015. p. 768–71.

26.

Sumonphan E, Auephanwiriyakul S, Theera-Umpon N. Interpretation of nevirapine concentration from immunochromatographic strip test using support vector regression. In: Proceedings of 2008 IEEE international conference on mechatronics and automation; 2008. p. 633–7.

27.

Tanaka R, Yuhi T, Nagatani N, Endo T, Kerman K, Takamura Y. A novel enhancement assay for immunochromatographic test strips using gold nanoparticles. Anal Bioanal Chem. 2006;385(8):1414–20.CrossRefPubMed

28.

Wei H, Dong Z. V4 neural network model for shape-based feature extraction and object discrimination. Cogn Comput. 2015;7(6):753–62.CrossRef

29.

Wei H, Li H. Shape description and recognition method inspired by the primary visual cortex. Cogn Comput. 2014;6(2):164–74.CrossRef

30.

Yager P, Edwards T, Fu E, Helton K, Nelson K, Tam MR, Weigl BH. Microfuidic diagnostic technologies for global public health. Nature. 2006;442:412–8.CrossRefPubMed

31.

Yang H, Wang Z, Shu H, Alsaadi FE, Hayat T. Almost sure \(H_{\infty }\) sliding mode control for nonlinear stochastic systems with Markovian switching and time-delays. Neurocomputing. 2016;175:392–400.CrossRef

32.

Yu Y, Dong H, Wang Z, Ren W, Alsaadi FE. Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties. Neurocomputing. 2016;182:18–24.CrossRef

33.

Yu D, Deng L. Deep learning and its applications to signal and information processing. IEEE Signal Process Mag. 2011;28(1):145–54.CrossRef

34.

Zeng N, Wang Z, Li Y, Du M, Liu X. Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering. IEEE Trans Biomed Eng. 2011;58(7):1959–66.CrossRefPubMed

35.

Zeng N, Wang Z, Li Y, Du M, Liu X. A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models. IEEE/ACM Trans Comput Biol Bioinf. 2012;9(2):321–9.CrossRef

36.

Zeng N, Wang Z, Li Y, Du M, Liu X. Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach. IEEE Trans Nanotechnol. 2012;11(2):321–7.CrossRef

37.

Zeng N, Wang Z, Li Y, Du M. Cellular neural networks for gold immunochromatographic strip image segmentation. Lect Notes Comput Sci. 2012;7231:110–20.CrossRef

38.

Zeng N, Wang Z, Li Y, Du M, Cao J, Liu X. Time series modeling of nano-gold immunochromatographic assay via expectation maximization algorithm. IEEE Trans Biomed Eng. 2013;60(12):3418–24.CrossRefPubMed

39.

Zeng N, Hung YS, Li Y, Du M. A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay. Expert Syst Appl. 2014;41(4):1708–15.CrossRef

40.

Zeng N, Wang Z, Zineddin B, Li Y, Du M, Xiao L, Liu X, Young T. Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach. IEEE Trans Med Imaging. 2014;33(5):1129–36.CrossRefPubMed

41.

Zeng N, Wang Z, Zhang H, Alsaadi Fuad E. A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cogn Comput. 2016;8(2):143–52.

42.

Zhao J, Du C, Sun H, Liu X, Sun J. Biologically motivated model for outdoor scene classification. Cogn Comput. 2013;7(1):20–33.CrossRef

43.

Zineddin B, Wang Z, Shi Y, Li Y, Du M, Liu X. A multi-view approach to cDNA microarray analysis. Int J Comput Biol Drug Des. 2010;3(2):91–111.CrossRefPubMed

Titel: Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip
verfasst von: Nianyin Zeng
Zidong Wang
Hong Zhang
Weibo Liu
Fuad E. Alsaadi
Publikationsdatum: 01.08.2016
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 4/2016
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-016-9404-x

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