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

arXiv: https://arxiv.org/abs/2207.01527 · HTML: https://mlsanity.com/paper/2207.01527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)
- [Adaptive Context Network for Scene Parsing](https://mlsanity.com/paper/1911.01664/) (2019)
- [Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge](https://mlsanity.com/paper/1705.09435/) (2017)

## Built on by (most cited first, 1 of 1)

- [Vision Transformers in Medical Imaging: A Review](https://mlsanity.com/paper/2211.10043/) (2022)

## Abstract (arXiv)

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.