Paper Info
Title
Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network.
Abstract
Gold immunochromatographic assay (GICA) is a widespread rapid detection method with less cost but high efficiency. It is easy to operate and dispense with professional staff and equipment, which conforms to the trend of point-of-care testing that advocated by modern medicine. With the development and progression of medical detection technology, the qualitative analysis that could be easily performed with the naked eye is not satisfying anymore. In recent years, improving the performance of quantitative analysis of the GICA has become a hot research topic. However, the GICA is susceptible to noise interference due to various factors when used in the qualitative analysis in clinics. The rise of artificial intelligence has provided us with new ideas and directions. As a popular neural network in deep learning, convolutional neural network (CNN) has achieved excellent results in image processing and has been widely applied to many fields, including biomedical engineering. In this paper, CNN is applied to the image segmentation of gold immunochromatographic strip. The grayscale features of the pre-processed images are learned by the established CNN network, and then, the control and test lines are accurately extracted and further quantitative analysis is performed. The results show that the method proposed in this paper has a good segmentation effect on the GICA, and it also provides a new scheme for the quantitative analysis of the GICA.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2893927
IEEE ACCESS
Keywords
Field
DocType
Gold immunochromatographic strip,quantitative analysis,image segmentation,CNN
Pattern recognition,Computer science,Convolutional neural network,Segmentation,Image processing,Image segmentation,Artificial intelligence,Deep learning,Artificial neural network,Grayscale,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Nianyin Zeng127821.91
Han Li223510.29
Yurong Li323416.14
Xin Luo456235.64