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DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection

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Abstract

With the advancement of artificial intelligence, CNNs have been successfully introduced into the discipline of medical data analyzing. Clinically, automatic pulmonary nodules detection remains an intractable issue since those nodules existing in the lung parenchyma or on the chest wall are tough to be visually distinguished from shadows, background noises, blood vessels, and bones. Thus, when making medical diagnosis, clinical doctors need to first pay attention to the intensity cue and contour characteristic of pulmonary nodules, so as to locate the specific spatial locations of nodules. To automate the detection process, we propose an efficient architecture of multi-task and dual-branch 3D convolution neural networks, called DBPNDNet, for automatic pulmonary nodule detection and segmentation. Among the dual-branch structure, one branch is designed for candidate region extraction of pulmonary nodule detection, while the other incorporated branch is exploited for lesion region semantic segmentation of pulmonary nodules. In addition, we develop a 3D attention weighted feature fusion module according to the doctor’s diagnosis perspective, so that the captured information obtained by the designed segmentation branch can further promote the effect of the adopted detection branch mutually. The experiment has been implemented and assessed on the commonly used dataset for medical image analysis to evaluate our designed framework. On average, our framework achieved a sensitivity of 91.33% false positives per CT scan and reached 97.14% sensitivity with 8 FPs per scan. The results of the experiments indicate that our framework outperforms other mainstream approaches.

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All data generated or analyzed during this study are included in this published article [46].

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) (61976123, 61601427); Taishan Young Scholars Program of Shandong Province; and Key Development Program for Basic Research of Shandong Province (ZR2020ZD44).

Funding

National Natural Science Foundation of China,61976123,Muwei Jian

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Muwei Jian, Haodong Jin & Linsong Zhang

  2. School of Information Science and Technology, Linyi University, Linyi, China

    Muwei Jian

  3. School of Control Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Haodong Jin & Hui Yu

  4. Medical Artificial Intelligence Research Center, Shandong University of Traditional Chinese Medicine, Qingdao, China

    Benzheng Wei

  5. School of Creative Technologies, University of Portsmouth, Portsmouth, UK

    Hui Yu

Authors
  1. Muwei Jian
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  4. Benzheng Wei
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Contributions

Muwei Jian: writing—conceptualization, methodology, writing—review and editing, supervision; Haodong Jin: software, validation, software, visualization, methodology; Linsong Zhang: formal analysis, data curation; Benzheng Wei: validation, data curation, visualization; Hui Yu: supervision, writing—review and editing, methodology.

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Correspondence to Muwei Jian.

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Jian, M., Jin, H., Zhang, L. et al. DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection. Med Biol Eng Comput 62, 563–573 (2024). https://doi.org/10.1007/s11517-023-02957-1

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  • DOI: https://doi.org/10.1007/s11517-023-02957-1

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