  With the development of computer technology, various models have emerged in
artificial intelligence. The transformer model has been applied to the field of
computer vision (CV) after its success in natural language processing (NLP).
Radiologists continue to face multiple challenges in today's rapidly evolving
medical field, such as increased workload and increased diagnostic demands.
Although there are some conventional methods for lung cancer detection before,
their accuracy still needs to be improved, especially in realistic diagnostic
scenarios. This paper creatively proposes a segmentation method based on
efficient transformer and applies it to medical image analysis. The algorithm
completes the task of lung cancer classification and segmentation by analyzing
lung cancer data, and aims to provide efficient technical support for medical
staff. In addition, we evaluated and compared the results in various aspects.
For the classification mission, the max accuracy of Swin-T by regular training
and Swin-B in two resolutions by pre-training can be up to 82.3%. For the
segmentation mission, we use pre-training to help the model improve the
accuracy of our experiments. The accuracy of the three models reaches over 95%.
The experiments demonstrate that the algorithm can be well applied to lung
cancer classification and segmentation missions.
