骨髓红粒细胞自动识别的深度学习模型

为了实现骨髓血细胞的自动识别, 构建了骨髓红系细胞和粒系细胞数据集, 基于深度学习语义分割技术提出了 CellNet 网络模型。 该模型通过加入残差模块增加了网络的深度, 利用卷积残差块使网络模型更容易训练, 并结合 U鄄Net 的裁剪操作为分割提供更精细的特征。 实验结果表明, 该模型对骨髓红系细胞和粒系细胞识别正确率分别达到 93. 65% 、95. 25% , 为骨髓血细胞自动识别技术提供了一种方法。

Deep Learning Model for Automatic Recognition of Erythroid Cells and Granulocyte Cells in Bone Marrow

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.