Abstract: In order to realize the automatic identification of bone marrow blood cells, bone marrow erythroid and granulocyte data sets are constructed, and a CellNet network model is proposed based on deep learning semantic segmentation technology. The model increases the depth of the network by adding a residual module, uses a convolution residual block to make the network model easier to train, and combines the U-Net clipping operation to provide more refined features for segmentation. The experimental results show that the correct recognition rate of this model for bone marrow erythroid cells and granulocytes reaches 93. 65% and 95. 25% , respectively, which provides a method for automatic identification of bone marrow blood cells.

Key words: bone marrow cells, cell morphology, cell classification, image recognition