Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip

被引：140

作者：

Zeng, Nianyin ^{[1
]}

Wang, Zidong ^{[2
,3
]}

Zhang, Hong ^{[1
]}

Liu, Weibo ^{[2
]}

Alsaadi, Fuad E. ^{[3
]}

机构：

[1] Xiamen Univ, Dept Mech & Elect Engn, Xiamen 361005, Fujian, Peoples R China

[2] Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England

[3] King Abdulaziz Univ, Commun Syst & Networks CSN Res Grp, Fac Engn, Jeddah 21589, Saudi Arabia

来源：

COGNITIVE COMPUTATION | 2016年 / 8卷 / 04期

关键词：

Gold immunochromatographic strip; Deep belief networks (DBNs); Restricted Boltzmann machine (RBM); Quantitative analysis; Image segmentation; STOCHASTIC-SYSTEMS; ASSAY; MODEL; PSO;

D O I：

10.1007/s12559-016-9404-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：684 / 692

页数：9

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