Paper Info
Title
Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip.
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.
Year
DOI
Venue
2016
https://doi.org/10.1007/s12559-016-9404-x
Cognitive Computation
Keywords
Field
DocType
Gold immunochromatographic strip,Deep belief networks (DBNs),Restricted Boltzmann machine (RBM),Quantitative analysis,Image segmentation
Image Inspection,Pattern recognition,Computer science,Deep belief network,Image processing,Image segmentation,Preprocessor,Artificial intelligence,Region of interest,Machine learning
Journal
Volume
Issue
ISSN
8
4
1866-9956
Citations 
PageRank 
References 
48
1.17
23
Authors
5
Name
Order
Citations
PageRank
Nianyin Zeng127821.91
Zidong Wang211003578.11
Hong Zhang327626.98
Weibo Liu452016.88
Fuad E. Alsaadi51818102.89