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LGDNet: local feature coupling global representations network for pulmonary nodules detection

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Abstract

Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing this task. However, existing deep convolutional neural networks exhibit limited long-range dependency capabilities and neglect crucial contextual information, resulting in reduced performance on detecting small-size nodules in CT scans. In this work, we propose a novel end-to-end framework called LGDNet for the detection of suspicious pulmonary nodules in lung CT scans by fusing local features and global representations. To overcome the limited long-range dependency capabilities inherent in convolutional operations, a dual-branch module is designed to integrate the convolutional neural network (CNN) branch that extracts local features with the transformer branch that captures global representations. To further address the issue of misalignment between local features and global representations, an attention gate module is proposed in the up-sampling stage to selectively combine misaligned semantic data from both branches, resulting in more accurate detection of small-size nodules. Our experiments on the large-scale LIDC dataset demonstrate that the proposed LGDNet with the dual-branch module and attention gate module could significantly improve the nodule detection sensitivity by achieving a final competition performance metric (CPM) score of 89.49%, outperforming the state-of-the-art nodule detection methods, indicating its potential for clinical applications in the early diagnosis of lung diseases.

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Funding

This work was supported by the National Natural Science Foundation of China under Grants 61901098 and 61971118, and Science and Technology Plan of Liaoning Province 2021JH1/10400051.

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Authors and Affiliations

  1. Faculty of Robot Science and Engineering, Northeastern University, Shenyang, Liaoning, 110167, China

    Jianning Chi, Jin Zhao, Siqi Wang, Xiaosheng Yu & Chengdong Wu

  2. Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, Liaoning, 110167, China

    Jianning Chi

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  1. Jianning Chi
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  2. Jin Zhao
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  3. Siqi Wang
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  4. Xiaosheng Yu
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  5. Chengdong Wu
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Contributions

All authors contributed to the study conception and design. Methodology was presented by Jianning Chi, Jin Zhao and Chengdong Wu. Material preparation, data collection and analysis were performed by Jianning Chi, Jian Miao, Jin Zhao, Siqi Wang and Xiaosheng Yu. The first draft of the manuscript was written by Jin Zhao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jianning Chi.

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Chi, J., Zhao, J., Wang, S. et al. LGDNet: local feature coupling global representations network for pulmonary nodules detection. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03043-w

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