A conditional generative adversarial network structure was proposed based on multi-scale discriminator to generate a large number of realistic leukocyte images, aiming at the problem of low detection accuracy due to insufficient leukocyte samples and unclear details of the generated cell images. The leukocyte images were added to the training set of the classification detection network to realize the generation and classification of blood cell images. A multi-scale convolution kernel and a multi-scale pooling domain were introduced into the authenticity discriminator of the generated confrontation network, and the channel connection which also improved the discriminator's ability to distinguish micro-detail texture features and macro-geometric features. The gradient similarity loss function was introduced to improve the brightness and edge clarity of the generated cell image to enhance the authenticity of the image. Results show that the quality of cell image is improved by the addition of multi-scale discriminator and gradient similarity loss function during the image generation stage. Compared with the case of real data training, the average accuracy of cell classification and detection is improved from 90.4% to 94.7% through increasing the diversity of cell samples during the image classification and detection stage.