Objective: This study aims to evaluate the effectiveness of a modified transformer model for autonomous recognition of breast nodules and generation of text reports. Methods: We constructed a transformer model to analyze ultrasound images of breast nodules of 832 patients (1284 nodules in total) and generate corresponding text reports. Additionally, we introduced the LGK dataset and compared our method with several state-of-the-art steganography methods. We evaluated the performance of the model using BLEU score. Results: In the LGK dataset, the BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores were 0.579, 0.391, 0.288 and 0.152, respectively. In breast nodule dataset, our model achieved BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of 0.547, 0.474, 0.352, and 0.282, respectively, which were higher than several other models. Conclusion: Our results indicate that the proposed transformer model can quickly identify breast nodules and generate standard reports independently. Compared with the existing state of the art methods, it achieved a higher BLEU score. The model also demonstrated excellent performance in the LGK dataset, indicating high text generalization capability.