Qualitative identification of fluorescence immunochromatography strips based on residual networks

Lingjun Zhong; Haiyan Jiang; Xianjing Xu

doi:10.1117/12.3006261

10 October 2023 Qualitative identification of fluorescence immunochromatography strips based on residual networks

Lingjun Zhong, Haiyan Jiang, Xianjing Xu

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127995U (2023) https://doi.org/10.1117/12.3006261
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The article proposes a qualitative identification scheme of fluorescent immunoassay strips based on residual networks to address problems such as poor strip positioning accuracy and inadequate strip size specifications in current photoelectric fluorescent immunoassay quantitative detection systems, which result in low accuracy and detection efficiency. The proposed method employs the Hough line detection algorithm, which is based on Canny edge detection, to extract the tilt angle of the test strip. It then combines this with contour extraction of the strip image to calculate its contour center. By utilizing the test strip tilt angle and contour center, the article accurately locates the test strip position. The residual network is utilized for extracting strip features, while the extreme learning machine is employed for discriminating the validity and positive/negative results of the fluorescent strips. Validation experiments on a novel coronavirus fluorescent immunoassay strip demonstrate that the residual network model based on extreme learning machine proposed in this article achieves 100% accuracy, precision, recall, and F1-score values for strip classification, effectively improving the recognition accuracy and detection efficiency of the fluorescent immunoassay quantitative detection system.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lingjun Zhong, Haiyan Jiang, and Xianjing Xu "Qualitative identification of fluorescence immunochromatography strips based on residual networks", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127995U (10 October 2023); https://doi.org/10.1117/12.3006261

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KEYWORDS

Image processing

Contour extraction

Detection and tracking algorithms

Education and training

Image filtering

Feature extraction

Edge detection

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