WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules
Abstract Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. Early and precise diagnosis is crucial for minimizing the risk of misdiagnosis and for effectively guiding clinical decision-making and monitoring...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12117-0 |
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| author | Yu Gu Haotian Bai Meng Chen Lidong Yang Baohua Zhang Jing Wang Xiaoqi Lu Jianjun Li Xin Liu Dahua Yu Ying Zhao Siyuan Tang Qun He |
| author_facet | Yu Gu Haotian Bai Meng Chen Lidong Yang Baohua Zhang Jing Wang Xiaoqi Lu Jianjun Li Xin Liu Dahua Yu Ying Zhao Siyuan Tang Qun He |
| author_sort | Yu Gu |
| collection | DOAJ |
| description | Abstract Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. Early and precise diagnosis is crucial for minimizing the risk of misdiagnosis and for effectively guiding clinical decision-making and monitoring treatment response. This study proposes the WSDC-ViT network to enhance computer-aided pneumonia detection and alleviate the diagnostic workload for radiologists. Unlike existing models such as Swin Transformer or CoAtNet, which primarily improve attention mechanisms through hierarchical designs or convolutional embedding, WSDC-ViT introduces a novel architecture that simultaneously enhances global and local feature extraction through a scalable self-attention mechanism and convolutional refinement. Specifically, the network integrates a scalable self-attention mechanism that decouples the query, key, and value dimensions to reduce computational overhead and improve contextual learning, while an interactive window-based attention module further strengthens long-range dependency modeling. Additionally, a convolution-based module equipped with a dynamic ReLU activation function is embedded within the transformer encoder to capture fine-grained local details and adaptively enhance feature expression. Experimental results demonstrate that the proposed method achieves an average classification accuracy of 95.13% and an F1-score of 95.63% on a chest X-ray dataset, along with 99.36% accuracy and a 99.34% F1-score on a CT dataset. These results highlight the model’s superior performance compared to existing automated pneumonia classification approaches, underscoring its potential clinical applicability. |
| format | Article |
| id | doaj-art-87e9115a91db4084aa0f047b6294d993 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-87e9115a91db4084aa0f047b6294d9932025-08-20T03:04:39ZengNature PortfolioScientific Reports2045-23222025-07-0115112110.1038/s41598-025-12117-0WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modulesYu Gu0Haotian Bai1Meng Chen2Lidong Yang3Baohua Zhang4Jing Wang5Xiaoqi Lu6Jianjun Li7Xin Liu8Dahua Yu9Ying Zhao10Siyuan Tang11Qun He12School of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Automation and Electrical Engineering, Inner Mongolia University of Science and TechnologySchool of Information and Electronics, Beijing Institute of TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Automation and Electrical Engineering, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologySchool of Digital and Intelligent Industry, Inner Mongolia University of Science and TechnologyAbstract Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. Early and precise diagnosis is crucial for minimizing the risk of misdiagnosis and for effectively guiding clinical decision-making and monitoring treatment response. This study proposes the WSDC-ViT network to enhance computer-aided pneumonia detection and alleviate the diagnostic workload for radiologists. Unlike existing models such as Swin Transformer or CoAtNet, which primarily improve attention mechanisms through hierarchical designs or convolutional embedding, WSDC-ViT introduces a novel architecture that simultaneously enhances global and local feature extraction through a scalable self-attention mechanism and convolutional refinement. Specifically, the network integrates a scalable self-attention mechanism that decouples the query, key, and value dimensions to reduce computational overhead and improve contextual learning, while an interactive window-based attention module further strengthens long-range dependency modeling. Additionally, a convolution-based module equipped with a dynamic ReLU activation function is embedded within the transformer encoder to capture fine-grained local details and adaptively enhance feature expression. Experimental results demonstrate that the proposed method achieves an average classification accuracy of 95.13% and an F1-score of 95.63% on a chest X-ray dataset, along with 99.36% accuracy and a 99.34% F1-score on a CT dataset. These results highlight the model’s superior performance compared to existing automated pneumonia classification approaches, underscoring its potential clinical applicability.https://doi.org/10.1038/s41598-025-12117-0Deep learningPneumoniaMedical image classificationVision transformer networkWindow interactionConvolution-Based module |
| spellingShingle | Yu Gu Haotian Bai Meng Chen Lidong Yang Baohua Zhang Jing Wang Xiaoqi Lu Jianjun Li Xin Liu Dahua Yu Ying Zhao Siyuan Tang Qun He WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules Scientific Reports Deep learning Pneumonia Medical image classification Vision transformer network Window interaction Convolution-Based module |
| title | WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules |
| title_full | WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules |
| title_fullStr | WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules |
| title_full_unstemmed | WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules |
| title_short | WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules |
| title_sort | wsdc vit a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules |
| topic | Deep learning Pneumonia Medical image classification Vision transformer network Window interaction Convolution-Based module |
| url | https://doi.org/10.1038/s41598-025-12117-0 |
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