Image Processing Technology Applied to Fluorescent Rapid Tests for Influenza A and B Viruses

This study establishes a detection method based on image recognition to interpret and quantitatively analyze fluorescent rapid test kits for influenza. The method operates in a dark chamber equipped with a UV-LED, where the fluorescence of the test kit is excited by the UV-LED and subsequently captured using a camera module. The captured images are processed by segmenting the regions of interest (ROI), converting them to grayscale images, and analyzing the grayscale value distributions to identify the control (C) and test (T) line regions. By comparing the values of the C and T lines, the concentration is determined to achieve quantitative analysis. In the linearity validation experiments, the concentrations of influenza A (H1N1) specimens are 2, 4, 6, 8, and 10 ng/mL, achieving a coefficient of determination (R2) of 0.9923. For influenza B (Yamagata) specimens, concentrations of 6, 8, 10, 12.5, and 25 ng/mL resulted in an R2 of 0.9878. The established method enables the detection of both influenza A (H1N1) and influenza B (Yamagata), replacing visual qualitative interpretation with quantitative analysis. Currently, the detection method developed in this paper is designed for use exclusively in a dark chamber and is specifically applied to fluorescent rapid tests. It cannot be directly used with conventional colloidal gold-based rapid test reagents. In the future, the proposed detection approach could be integrated with neural networks to enable its application to non-fluorescent rapid test interpretation and to operate beyond the dark chamber environment, for example by utilizing smartphone imaging for result interpretation under normal lighting conditions.