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Methodological study of quantitative immunochromatographic detection based on optimised flow measurement

Published: 26 March 2024 Publication History

Abstract

Flow measurement immunochromatography has been widely used in the field of medical testing due to its advantages of simplicity, speed, convenience and low cost. The combination of machine vision image processing technology and test strip technology has become a research hotspot for rapid quantitative detection in recent years. This paper reviews the research progress of flow measurement immunochromatography to improve the accuracy in the past five years, evaluates the advantages and disadvantages of flow measurement immunochromatography detection from the perspectives of test strip labelling methods, image segmentation process and deep learning algorithms, and analyses its future development direction.

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EBIMCS '23: Proceedings of the 2023 6th International Conference on E-Business, Information Management and Computer Science
December 2023
265 pages
ISBN:9798400709333
DOI:10.1145/3644479
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 26 March 2024

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Author Tags

  1. Deep learning
  2. Feature extraction
  3. Image recognition
  4. Immunochromatography
  5. Test strip preparation

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EBIMCS 2023

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Overall Acceptance Rate 143 of 708 submissions, 20%

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