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Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip

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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.

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Acknowledgments

This work was supported in part by the Natural Science Foundation of China under Grant 61403319, in part by the Fujian Natural Science Foundation under Grant 2015J05131, in part by the Fujian Provincial Key Laboratory of Eco-Industrial Green Technology and in part by the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361005, Fujian, China

    Nianyin Zeng & Hong Zhang

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang & Weibo Liu

  3. Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Zidong Wang & Fuad E. Alsaadi

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  1. Nianyin Zeng
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  2. Zidong Wang
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  3. Hong Zhang
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  4. Weibo Liu
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  5. Fuad E. Alsaadi
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Correspondence to Nianyin Zeng.

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Conflict of Interest

Nianyin Zeng, Zidong Wang, Hong Zhang, Weibo Liu and Fuad E. Alsaadi declare that they have no conflict of interest.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

Human and Animal Rights

This article does not contain any studies with human or animal subjects performed by the any of the authors.

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Zeng, N., Wang, Z., Zhang, H. et al. Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip. Cogn Comput 8, 684–692 (2016). https://doi.org/10.1007/s12559-016-9404-x

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  • DOI: https://doi.org/10.1007/s12559-016-9404-x

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