Open AccessQuantitative Analysis Method of Immunochromatographic Strip Based on Reinforcement Learning
Author(s) -
Songming Liu,
Shanshan Zeng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1449/1/012058
Subject(s) - computer science , artificial intelligence , reinforcement learning , segmentation , grayscale , pattern recognition (psychology) , computer vision , image segmentation , machine learning , image (mathematics)
Gold immunochromatographic assay (GICA) is a widely used immunological detection technology with high sensitivity and high efficiency as well as simple operation. Traditional GICA is mainly based on instrument for qualitative detection. In response to the above questions, this paper aims to develop an automated detection framework for immunochromatographic strips that can adaptively improve the detection performance of the gold immunochromatographic strip (GICS) system. As a research hotspot of machine learning (ML), reinforcement learning (RL) has made many progresses in the field of image segmentation. In this paper, the RL method is applied to the GICS system for the first time. The RL agent provides an adaptive segmentation model for the newly obtained GICS images by learning the state features of the preprocessed images. It is worth noting that our method does not require a large training data set and simultaneously can effectively reduce the grayscale-based image feature space. The experimental results show that the proposed method has a good segmentation effect, and provides a reliable solution for the quantitative analysis of the GICS system.