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DC Field | Value | Language |
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dc.contributor.author | Zeng, N | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Zhang, H | - |
dc.contributor.author | Liu, W | - |
dc.contributor.author | Alsaadi, FE | - |
dc.date.accessioned | 2016-06-16T12:06:30Z | - |
dc.date.available | 2016-04-30 | - |
dc.date.available | 2016-06-16T12:06:30Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Cognitive Computation, 8(4): pp. 1 - 9, (2016) | en_US |
dc.identifier.issn | 1866-9956 | - |
dc.identifier.uri | http://link.springer.com/article/10.1007%2Fs12559-016-9404-x | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/12807 | - |
dc.description.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. | en_US |
dc.description.sponsorship | 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. | en_US |
dc.format.extent | 1 - 9 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.subject | Gold immunochromatographic strip | en_US |
dc.subject | Deep belief networks (DBNs) | en_US |
dc.subject | Restricted boltzmann machine (RBM) | en_US |
dc.subject | Quantitative analysis | en_US |
dc.subject | Image segmentation | en_US |
dc.title | Deep belief networks for quantitative analysis of a gold immunochromatographic strip | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s12559-016-9404-x | - |
dc.relation.isPartOf | Cognitive Computation | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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Fulltext.pdf | File is embargoed until 30/04/2017 | 3.99 MB | Adobe PDF | View/Open |
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