## 4 Conclusions  
We developed an image processing-based quantitative lateral flow immunoassay detection method. A dark box was designed to control the lighting conditions, and the accuracy of the algorithm under different lighting conditions was analyzed. Different models of smartphones were used to take photos, and the differences in the images were compared. An intelligent smartphone algorithm was designed and applied to a mobile app. The algorithm focuses on intelligently finding the optimal threshold to segment images. This method first identifies the control line with the most distinct color and then uses various factors, such as pixel mutation position, control line position, and width, and actual design distance of the test strip as the discriminant factors to intelligently locate the lighter-colored test line. The algorithm achieves an accuracy rate of 94.23% when tested on 260 test strips. After extracting the characteristic values, a mathematical model based on SVM was selected to fit the characteristic values that best correlate with concentration, yielding an \(R^{2}\) value of 0.99492. To test the sensitivity, robustness, and effectiveness of the algorithm, the images were divided into three categories based on sample concentration, and random noise was added for testing. It was found that the proposed algorithm maintains an accuracy rate of over 90% for detecting medium-to-high-concentration test strips with darker colors, and there is a slight decrease in performance for low-concentration test strips, but the overall accuracy rate still reaches 85%. The detection algorithm proposed in this study was compared with popular neural network algorithms, such as KNN, CNN, and U-net, and traditional image processing algorithms. It was found that the proposed algorithm can achieve comparable accuracy to the neural network algorithms with a small sample size. It also exhibits higher stability and lower algorithm complexity. However, there are still significant limitations to the algorithm proposed in this study. Only three models of smartphones were used in the study, and more lighting conditions need to be compared. The algorithm also needs to be adjusted when testing strips of different specifications. After adding noise to lighter-colored test strips for testing, it was found that the detection accuracy decreases significantly, and efforts need to be made to improve the accurate measurement of low-concentration test strips. This study focuses on a sample of 260 SAA immunoassay test strips, which is still insufficient, and more samples need to be added to increase persuasiveness. Finally, this algorithm is currently suitable for personal rapid testing. However, in the future, it could be used in broader application scenarios, such as large-scale rapid testing in hospitals and community health centers, providing better technical support for disease prevention and control. In addition, the algorithm could also be applied to other types of detection, such as chemiluminescence assays [23] or enzyme-linked immunosorbent assays [24], with potential application prospects and development potential. **Author Contributions:** Conceptualization, S.Z. ; Methodology, X.J. and G.Y. ; Software, X.J. and S.L. ; Investigation, S.W. and L.C. ; Writing--original draft preparation, X.J. ; Writing--review and editing, H.P. ; Supervision, S.Z. ; Project administration, S.Z. ; funding acquisition, H.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the National Natural Science Foundation of China (22064008), the Key Research and Development Program of Guilin (2020010323), and the Scientific Research and Technology Development Plan of Guilin (20210217-1). **Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Guilin University of Technology. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.