Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on an Improved Swin Transformer

Abstract

With the advancement of computer technology, transformer models have been applied to the field of computer vision (CV) after their success in natural language processing (NLP). In today’s rapidly evolving medical field, radiologists continue to face multiple challenges, such as increased workload and increased diagnostic demands. The accuracy of traditional lung cancer detection methods still needs to be improved, especially in realistic diagnostic scenarios. In this study, we evaluated the performance of the Swin Transformer model in the classification and segmentation of lung cancer. The results showed that the pre-trained Swin-B model achieved a top-1 accuracy of 82.26% in the classification mission, outperforming ViT by 2.529%. In the segmentation mission, the Swin-S model demonstrated improvement over other methods in terms of mean Intersection over Union (mIoU). These results suggest that pre-training can be an effective approach for improving the accuracy of the Swin Transformer model in these tasks.