Abstract
The ovulation test strip is a tool for ovulation detection. At present, many APPs have been developed to analyze the photos taken from ovulation test strips to read the T/C ratio for detecting the level of luteinizing hormone (LH) in human urine. However, in the process of detecting the T and C lines, the background colors of the area near the T line or C line may be red, the T and C lines are sometimes fuzzy, and the colors of the T and C lines are sometimes distributed unevenly. In these cases, these APPs will be confronting difficulty to accurately detect the T and C lines and further read their ratio. To solve these problems, we proposed a method that consists of two steps. The first step is to use Mask R-CNN to locate the T and C lines, and the second step is to use a trained Pseudo-Siamese ratio reading network (PSRRNet) to read the ratio value based on the output results of the first step. The proposed PSRRNet consists of a Pseudo-Siamese network for simultaneously extracting the features of the T and C lines and a fully connected network to use the extracted features to predict the T/C ratio value. The experimental results illustrated that the proposed method suits to handle ovulation test strip reading.
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Acknowledgment
This work was partially supported by the Innovation Project of GUET Graduate Education (2020YCXS057), Natural Science Foundation of Guangxi District (2018GXNSFDA138006), the National Natural Science Foundation of China (61866007), and Image Intelligent Processing Project of Key Laboratory Fund (GIIP201505).
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Wang, L., He, L., Li, J., Zeng, D., Wen, Y. (2021). T Line and C Line Detection and Ratio Reading of the Ovulation Test Strip Based on Deep Learning. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_60
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DOI: https://doi.org/10.1007/978-3-030-91608-4_60
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