Automatic cells counting method based on machine learning and deep learning

Kangli Zhang; Taiyu Gao; Xiaoyi Feng; Yinglei Mei; Jiayuan Li

doi:10.1117/12.2641105

3 October 2022 Automatic cells counting method based on machine learning and deep learning

Kangli Zhang, Taiyu Gao, Xiaoyi Feng, Yinglei Mei, Jiayuan Li

Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 122900X (2022) https://doi.org/10.1117/12.2641105
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China

Abstract

Cell counting is a fundamental study in cell culture research and an essential step in many cell experiments to determine cell numbers and survival rates. This paper uses two different methods to count cells, including machine learning and the deep learning method. It compares the differences, advantages, and disadvantages of the two methods in cell counting through experimental data comparison. In the first method, we use image preprocessing and open-source software to count cell images. The second method is based on CNN cell counting. We input 200, 400, 800, and 1000 pictures for training, selected 268 pictures as the test set, predicted the results, and got a training model with an accuracy of about 91%. Finally, we compared the experimental results and analyzed the pros and cons of the two methods.

Citation Download Citation

Kangli Zhang, Taiyu Gao, Xiaoyi Feng, Yinglei Mei, and Jiayuan Li "Automatic cells counting method based on machine learning and deep learning", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 122900X (3 October 2022); https://doi.org/10.1117/12.2641105

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