Abstract
Red blood cell (RBC) counting is of great medical significance in clinical examination. Commonly, the cell counting task is completed by microscopic examination, which requires a high resolution. This paper proposes an automatic counting system of red blood cells based on Fourier ptychographic microscopy (FPM) and estimates the RBC number via a convolutional neural network (CNN). The counting network is based on a regression model, using a VGG-16 network combined with a feature pyramid network (FPN). The experimental results show that the mean absolute percentage error (MAPE) of our counting network can reach 0.86%, which means a high accuracy.
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Acknowledgements
This work was supported by Key Laboratory Foundation under Grant TCGZ2020C004
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Wang, S., Xu, T., Zhang, J., Wang, X., Chen, Y., Zhang, J. (2021). Automatic Counting System of Red Blood Cells Based on Fourier Ptychographic Microscopy. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_119
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DOI: https://doi.org/10.1007/978-981-15-8411-4_119
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