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

TL;DR

This paper introduces an improved transformer-based algorithm for lung cancer image classification and segmentation, achieving high accuracy and supporting medical diagnosis with efficient computational methods.

Contribution

It proposes a novel segmentation method based on an improved Swin Transformer tailored for medical image analysis, enhancing accuracy in lung cancer detection.

Findings

Maximum classification accuracy of 82.3% with Swin models.

Segmentation accuracy exceeds 95% with pre-training.

Algorithm effectively supports lung cancer diagnosis.

Abstract

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.