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Cognitive Computation

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01-08-2016

Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip

Published in: Cognitive Computation | Issue 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.

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Title: Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip
Publication date: 01-08-2016
Published in: Cognitive Computation / Issue 4/2016
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-016-9404-x

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