Although blood cell analyzers have been widely used in hospitals, the manual microscopy is still the “ gold standard” for leukocyte detection. In this article, T-DETR, a DETR-based deep learning model with Transformer architecture is proposed for the detection of peripheral blood leukocytes, which aims to relieve the pressure of manual microscopy. First, PVTv2 is used as the backbone of DETR to extract multi-scale feature maps to improve detection accuracy. Then, the deformable attention module is introduced into the DETR model to reduce the computational complexity to accelerate the model convergence. Finally, to obtain the optimal weights, the training method of transfer learning is used on the filtered public leukocyte dataset. Experimental results show that T-DETR has an mAP of 0. 476 on the COCO dataset and 0. 954 on the leukocyte dataset, which is better than DETR and the classical CNN model. Results verify the feasibility of the Transformer structured model for medical image detection applications.