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Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network

文献类型：期刊论文

作者Zeng, Nianyin1; Li, Han1; Li, Yurong2,3; Luo, Xin4,5
刊名IEEE ACCESS
出版日期2019
卷号7页码:16257-16263
关键词Gold immunochromatographic strip quantitative analysis image segmentation CNN
ISSN号2169-3536
DOI10.1109/ACCESS.2019.2893927
通讯作者Zeng, Nianyin(zny@xmu.edu.cn) ; Li, Yurong(liyurong@fzu.edu.cn) ; Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要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.
资助项目Natural Science Foundation of China[61773124] ; Open Fund of Provincial Key Laboratory of Eco-Industrial Green Technology-Wuyi University ; UK-China Industry Academia Partnership Programme[UK-CIAPP-276]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000459116300005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/7465]  
专题大数据挖掘及应用中心
通讯作者Zeng, Nianyin; Li, Yurong; Luo, Xin
作者单位1.Xiamen Univ, Dept Instrumental & Elect Engn, Xiamen 361005, Peoples R China
2.Fuzhou Univ, Coll Elect Engn & Automat, Fuzhou 350002, Fujian, Peoples R China
3.Fuzhou Univ, Fujian Key Lab Med Instrumentat & Pharmaceut Tech, Fuzhou 350002, Fujian, Peoples R China
4.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
5.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Nianyin,Li, Han,Li, Yurong,et al. Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network[J]. IEEE ACCESS,2019,7:16257-16263.
APA Zeng, Nianyin,Li, Han,Li, Yurong,&Luo, Xin.(2019).Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network.IEEE ACCESS,7,16257-16263.
MLA Zeng, Nianyin,et al."Quantitative Analysis of Immunochromatographic Strip Based on Convolutional Neural Network".IEEE ACCESS 7(2019):16257-16263.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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