Cross-ViT based benign and malignant classification of pulmonary nodules.
The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmonary nodules, but traditional CNN models focus mor...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318670 |
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| _version_ | 1823861028598841344 |
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| author | Qinfang Zhu Liangyan Fei |
| author_facet | Qinfang Zhu Liangyan Fei |
| author_sort | Qinfang Zhu |
| collection | DOAJ |
| description | The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmonary nodules, but traditional CNN models focus more on the local features of pulmonary nodules and lack the extraction of global features of pulmonary nodules. To solve this problem, a Cross fusion attention ViT (Cross-ViT) network that fuses local features extracted by CNN and global features extracted by Transformer is proposed. The network first extracts different features independently through two branches and then performs feature fusion through the Cross fusion attention module. Cross-ViT can effectively capture and process both local and global information of lung nodules, which improves the accuracy of classifying the benign and malignant nature of pulmonary nodules. Experimental validation was performed on the LUNA16 dataset, and the accuracy, precision, recall and F1 score reached 91.04%, 91.42%, 92.45% and 91.92%, respectively, and the accuracy, precision, recall and F1 score with SENet as CNN branch reached 92.43%, 94.27%, 91.68% and 92.96%, respectively. The results show that the accuracy, precision, recall and F1 score of the proposed method are 0.3%, 0.11%, 4.52% and 3.03% higher than those of the average optimal method, respectively, and the performance of Cross-ViT network for benign and malignant classification is better than most classification methods. |
| format | Article |
| id | doaj-art-6273241df6cb4eda9e97bbc3c4a6801c |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-6273241df6cb4eda9e97bbc3c4a6801c2025-02-10T05:30:37ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031867010.1371/journal.pone.0318670Cross-ViT based benign and malignant classification of pulmonary nodules.Qinfang ZhuLiangyan FeiThe benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmonary nodules, but traditional CNN models focus more on the local features of pulmonary nodules and lack the extraction of global features of pulmonary nodules. To solve this problem, a Cross fusion attention ViT (Cross-ViT) network that fuses local features extracted by CNN and global features extracted by Transformer is proposed. The network first extracts different features independently through two branches and then performs feature fusion through the Cross fusion attention module. Cross-ViT can effectively capture and process both local and global information of lung nodules, which improves the accuracy of classifying the benign and malignant nature of pulmonary nodules. Experimental validation was performed on the LUNA16 dataset, and the accuracy, precision, recall and F1 score reached 91.04%, 91.42%, 92.45% and 91.92%, respectively, and the accuracy, precision, recall and F1 score with SENet as CNN branch reached 92.43%, 94.27%, 91.68% and 92.96%, respectively. The results show that the accuracy, precision, recall and F1 score of the proposed method are 0.3%, 0.11%, 4.52% and 3.03% higher than those of the average optimal method, respectively, and the performance of Cross-ViT network for benign and malignant classification is better than most classification methods.https://doi.org/10.1371/journal.pone.0318670 |
| spellingShingle | Qinfang Zhu Liangyan Fei Cross-ViT based benign and malignant classification of pulmonary nodules. PLoS ONE |
| title | Cross-ViT based benign and malignant classification of pulmonary nodules. |
| title_full | Cross-ViT based benign and malignant classification of pulmonary nodules. |
| title_fullStr | Cross-ViT based benign and malignant classification of pulmonary nodules. |
| title_full_unstemmed | Cross-ViT based benign and malignant classification of pulmonary nodules. |
| title_short | Cross-ViT based benign and malignant classification of pulmonary nodules. |
| title_sort | cross vit based benign and malignant classification of pulmonary nodules |
| url | https://doi.org/10.1371/journal.pone.0318670 |
| work_keys_str_mv | AT qinfangzhu crossvitbasedbenignandmalignantclassificationofpulmonarynodules AT liangyanfei crossvitbasedbenignandmalignantclassificationofpulmonarynodules |