In many biological laboratories, biologists analyze images and identify cell or organ states manually. There are some problems: lack of human resource and high experimental costs, among others. Identification results vary according to the person. To solve these problems, the process automation of biologists’ operations and quantitative identification are needed. Here, a cell-foci-phenotype identification system is developed by applying image processing and machine learning methods to fluorescent cell images. With this system, cell-foci-phenotype with high accuracy can be predicted and biologists’ efforts in doing image analysis can be reduced.

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