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20 October 2022 Swin transformer based benign and malignant pulmonary nodule classification
Panpan Wu, Jianming Chen, Yichen Wu
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Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124512F (2022) https://doi.org/10.1117/12.2656809
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. Early detection and diagnosis of pulmonary nodules are crucial to improve the patients’ relative survival rate. Although existing deep learning-based methods have achieved good results in distinguishing benign and malignant pulmonary nodules, most works mainly focus on designing new models to obtain deeper features, rather than on effective feature representations of the discriminative pulmonary nodules. In the present work, a benign and malignant pulmonary nodule classification method based on Swin Transformer model is developed. The multi-head self-attention hierarchical architecture of Swin Transformer capable of flexible modeling and linear computational complexity, enables the deep models simultaneously extract the local and global features, paying more attention on the key areas with a large amount of information in CT images, while suppressing other irrelevant information. The experimental results on LIDC-IDRI dataset demonstrate that the presented model is an effective and competitive method for the classification task of benign and malignant pulmonary nodules, compared with recent state-of-the-art approaches.
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Panpan Wu, Jianming Chen, and Yichen Wu "Swin transformer based benign and malignant pulmonary nodule classification", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124512F (20 October 2022); https://doi.org/10.1117/12.2656809
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KEYWORDS
Transformers
Tumor growth modeling
Computed tomography
Lung
Lung cancer
Feature extraction
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