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Res-trans networks for lung nodule classification

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Lung cancer usually presents as pulmonary nodules on early diagnostic images, and accurately estimating the malignancy of pulmonary nodules is crucial to the prevention and diagnosis of lung cancer. Recently, deep learning algorithms based on convolutional neural networks have shown potential for pulmonary nodules classification. However, the size of the nodules is very diverse, ranging from 3 to 30 mm, which makes classifying them to be a challenging task. In this study, we propose a novel architecture called Res-trans networks to classify nodules in computed tomography (CT) scans.

Methods

We designed local and global blocks to extract features that capture the long-range dependencies between pixels to adapt to the correct classification of lung nodules of different sizes. Specifically, we designed residual blocks with convolutional operations to extract local features and transformer blocks with self-attention to capture global features. Moreover, the Res-trans network has a sequence fusion block that aggregates and extracts the sequence feature information output by the transformer block that improves classification accuracy.

Results

Our proposed method is extensively evaluated on the public LIDC-IDRI dataset, which contains 1,018 CT scans. A tenfold cross-validation result shows that our method obtains better performance with AUC = 0.9628 and Accuracy = 0.9292 compared with recently leading methods.

Conclusion

In this paper, a network that can capture local and global features is proposed to classify nodules in chest CT. Experimental results show that our proposed method has better classification performance and can help radiologists to accurately analyze lung nodules.

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Data availability

This article used the LIDC-IDRI public dataset.

References

  1. Siegel RL, Miller KD (2019) Jemal, A (2019) Cancer statistics. CA Cancer J Clin 69(1):7–34. https://doi.org/10.3322/caac.21551

    Article  PubMed  Google Scholar 

  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424. https://doi.org/10.3322/caac.21492

    Article  PubMed  Google Scholar 

  3. Zheng S, Shen Z, Peia C, Ding W, Huang L (2021) Interpretative computer-aided lung cancer diagnosis: from radiology analysis to malignancy evaluation. Comput Meth Programs Biomed. https://doi.org/10.1016/j.cmpb.2021.106363

    Article  Google Scholar 

  4. Ren Y, Tsai MY, Chen L, Wang J, Li S, Liu Y, Jia X, Shen C (2020) A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification. Int J Comput Assist Radiol Surg 15:287–295. https://doi.org/10.1007/s11548-019-02097-8

    Article  PubMed  Google Scholar 

  5. Liao F, Liang M, Li Z, Hu X, Song S (2019) Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE Trans Neural Netw Learn Syst 30(11):3484–3495. https://doi.org/10.1109/TNNLS.2019.2892409

    Article  PubMed  Google Scholar 

  6. Jiang H, Gao F, Xu X, Huang F, Zhu S (2020) Attentive and ensemble 3D dual path networks for pulmonary nodules classification. Neurocomputing 398:422–430. https://doi.org/10.1016/j.neucom.2019.03.103

    Article  Google Scholar 

  7. Zheng S, Guo J, Cui X, Veldhuis RNJ, Oudkerk M, van Ooijen PMA (2020) Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imag 39(3):797–805. https://doi.org/10.1109/TMI.2019.2935553

    Article  Google Scholar 

  8. Zhang J, Xia Y, Zeng H, Zhang Y (2018) NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection. Neurocomputing 317:159–167. https://doi.org/10.1016/j.neucom.2018.08.022

    Article  Google Scholar 

  9. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Tian J (2017) Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 61:663–673. https://doi.org/10.1016/j.patcog.2016.05.029

    Article  Google Scholar 

  10. Zhao X, Liu L, Qi S, Teng Y, Li J, Qian W (2018) Agile convolutional neural network for pulmonary nodule classification using CT images. Int J Comput Assist Radiol Surg. https://doi.org/10.1007/s11548-017-1696-0

    Article  PubMed  Google Scholar 

  11. Jiang H, Shen F, Gao F, Han W (2021) Learning efficient, explainable and discriminative representations for pulmonary nodules classification. Pattern Recognit 113:107825. https://doi.org/10.1016/j.patcog.2021.107825

    Article  Google Scholar 

  12. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition. IEEE, pp 770–778

  14. Lee SLA, Kouzani AZ, Hu EJ (2016) Random Forest based lung nodule classification aided by clustering. Comput Med Imaging Graph 34(7):535–542. https://doi.org/10.1016/j.compmedimag.2010.03.006

    Article  Google Scholar 

  15. Akram S, Javed MY, Hussain A, Riaz F, Usman Akram M (2015) Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques. J Exp Theor Arti Intell 27(6):737–751. https://doi.org/10.1080/0952813X.2015.1020526

    Article  Google Scholar 

  16. Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg 12(10):1799–1808. https://doi.org/10.1007/s11548-017-1605-6

    Article  PubMed  Google Scholar 

  17. Shen S, Han SX, Aberle DR, Bui AA, Hsu W (2019) An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Syst Appl 128:84–95. https://doi.org/10.1016/j.eswa.2019.01.048

    Article  PubMed  PubMed Central  Google Scholar 

  18. Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, Cai W (2018) Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans Med Imaging 38(4):991–1004

    Article  PubMed  Google Scholar 

  19. Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Clarke LP (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931. https://doi.org/10.1118/1.3528204

    Article  PubMed  PubMed Central  Google Scholar 

  20. Al-Shabi M, Shak K, Tan M (2021) 3D axial-attention for lung nodule classification. Int J CARS 16:1319–1324. https://doi.org/10.1007/s11548-021-02415-z

    Article  Google Scholar 

  21. Al-Shabi M, Lan BL, Chan WY, Ng KH, Tan M (2019) Lung nodule classification using deep local–global networks. Int J Comput Assist Radiol Surg 14(10):1815–1819. https://doi.org/10.1007/s11548-019-01981-7

    Article  PubMed  Google Scholar 

  22. Wang X, Girshick R, Gupta A, He K. (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7794–7803

  23. Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops pp 0–0. IEEE

  24. Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011–2023. https://doi.org/10.1109/TPAMI.2019.2913372

    Article  PubMed  Google Scholar 

  25. Chen M, Peng H, Fu J, Ling H (2021) Autoformer: Searching transformers for visual recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12270–12280.

  26. Sun P, Zhang R, Jiang Y, Kong T, Xu C, Zhan W, Luo P (2021).Sparse r-cnn: End-to-end object detection with learnable proposals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14454–14463

  27. Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J´ egou, H (2021) Training data-efficient image transformers distillation through attention. In: International Conference on Machine Learning. PMLR, pp 10347–10357.

  28. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp 618–626

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Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University, Zhengzhou, China

    Dongxu Liu, Yun Tie & Lin Qi

  2. Department of Respiratory and Sleep Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Fenghui Liu

  3. Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Feng Wang

Authors
  1. Dongxu Liu
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  2. Fenghui Liu
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  3. Yun Tie
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  4. Lin Qi
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  5. Feng Wang
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Correspondence to Yun Tie.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Cite this article

Liu, D., Liu, F., Tie, Y. et al. Res-trans networks for lung nodule classification. Int J CARS 17, 1059–1068 (2022). https://doi.org/10.1007/s11548-022-02576-5

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