Lung lesion detection and disease diagnosis based on chest X-ray images(DR) is a routine clinical operation. For pulmonary tuberculosis patients, the lesion area of tuberculosis in the DR image is highly compatible with the background, the target diffusion is serious, and the edge shape is extremely irregular, which seriously interferes with the accuracy of diagnosis. To solve the above problems, a Tuberculosis Deep Transfer Net(TDT-Net)integrating the imaging characteristics of pneumonia is proposed. Using the characteristics that tuberculosis and COVID-19 are respiratory infectious diseases and have similar imaging manifestations on DR images, with the help of a large number of pneumonia DR images, strong correlation features are introduced through transfer learning to improve the detection accuracy of tuberculosis lesions. TDT-Net combines transformer and extended residual technology, and proposes a context-aware enhancement module to strengthen the modeling ability of the migration model for global context information; The feature refinement module is used to reduce the redundant information introduced in the transfer process and highlight the representation of strongly related features. The experimental results show that on the TBK11k dataset, the Average Precision (AP) of the proposed detection method reaches 87.5%, and the Recall reaches 80.7%. Compared with the networks such as YOLOV5 and RetinaNet, the detection accuracy of tuberculosis lesions is effectively improved, and more accurate localization and classification of tuberculosis lesions are achieved.