The task of the objects identification, counting, and measurement is a huge part of scientific investigations and technological applications. Automated methods using traditional processing such as segmentation, edge detection, and so on represented by available software (e.g. CellProfiler) are not flexible, can be used only with images of high-quality, and in addition require setting a part of parameters by hand. This contribution presents the applying the deep learning method for recognition of HeLa cells expressing green fluorescent protein (EGFP) automatically. We used Cascade Mask R-CNN neural networks which has a ResNeXt backbone and deformable convolutional networks layers. Training dataset contained seven pictures with 5754 labeled cells. Three images with 2469 labeled cells were used as test-dataset. The trained neural network showed mAP=0.4.