Comparison of deep learning algorithms using non-local methods for lung nodule classification

Research Article
Open access

Comparison of deep learning algorithms using non-local methods for lung nodule classification

Peiyu Wang 1
  • 1 School of Computer Science, University of Nottingham, Ningbo, China, Zhejiang, 315000, China    
  • *corresponding author
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

Lung cancer has been identified as a serious and fatal disease due to its high morbidity and mortality. It is of vital importance for lung cancer patients to obtain early detection of the disease so that the later treatments may bring good effects. Lung computed tomography (CT), as a normal method to diagnose the disease, can be used to recognize typical lung cancer, but it is possible to confuse cancer with some other diseases, such as innocent tumour and phthisis. Therefore, an accurate diagnostic tool is required to help clinical disease recognition. Machine learning (ML), especially deep learning (DL) is an ideal technique to classify CT images thanks to the great capability of image processing and feature recognition. However, the task for ML faces several challenges that have made a negative difference in the accuracy of algorithms. The main problem is that lung nodules can have heterogeneous sizes and shapes varying in a wide range, thus both local and global features of data should be considered to enhance the classification results. In this work, the author investigated advanced works in the territory of lung cancer identification using ML and the comparison results of newly proposed models and proper analysis are provided. In addition, possible future improvements are discussed.

Keywords:

Computer Vision, Deep learning, Non-local network, Computer-Aided Diagnoses, Lung Nodule classification.

Wang,P. (2023). Comparison of deep learning algorithms using non-local methods for lung nodule classification. Applied and Computational Engineering,4,743-749.
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References

[1]. Lung cancer. nhs.uk. 2022. Retrieved 19 August 2022, from https://www.nhs.uk/conditions/lung- cancer/.

[2]. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. 2016. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5), 1299-1312.

[3]. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. (LeNet)

[4]. Simonyan, K., and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. (VGG)

[5]. He, K., Zhang, X., Ren, S., and Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). (ResNet)

[6]. Al-Shabi, M., Shak, K., and Tan, M. 2022. ProCAN: Progressive growing channel attentive non- local network for lung nodule classification. Pattern Recognition, 122, 108309.

[7]. Armato III, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., ... and Clarke, L. P. 2011. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical physics, 38(2), 915-931.

[8]. Zhu, W., Liu, C., Fan, W., and Xie, X. 2018. Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. In 2018 IEEE winter conference on applications of computer vision (WACV) (pp. 673-681). IEEE. (DeepLung)

[9]. Al-Shabi, M., Lan, B. L., Chan, W. Y., Ng, K. H., and Tan, M. 2019. Lung nodule classification using deep local–global networks. International journal of computer assisted radiology and surgery, 14(10), 1815-1819. (Local-Global)

[10]. Beini, Z., Xuee, C., Bo, L., and Weijia, W. 2021. A new few-shot learning method of digital PCR image detection. IEEE Access, 9, 74446-74453.

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

[12]. Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. 2019. Self-attention generative adversarial networks. In International conference on machine learning (pp. 7354-7363). PMLR. (selfattention)

[13]. Karras, T., Aila, T., Laine, S., and Lehtinen, J. 2017. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196. (ProGAN)

[14]. Xie, Y., Xia, Y., Zhang, J., Song, Y., Feng, D., Fulham, M., and Cai, W. 2018. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE transactions on medical imaging, 38(4), 991-1004. (MV-KBC)

[15]. Xie, Y., Zhang, J., and Xia, Y. 2019. Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT. Medical image analysis, 57, 237-248. (MV- SSAC)

[16]. Al-Shabi, M., Lee, H. K., and Tan, M. 2019. Gated-dilated networks for lung nodule classification in CT scans. IEEE Access, 7, 178827-178838.

Cite this article

Wang,P. (2023). Comparison of deep learning algorithms using non-local methods for lung nodule classification. Applied and Computational Engineering,4,743-749.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Lung cancer. nhs.uk. 2022. Retrieved 19 August 2022, from https://www.nhs.uk/conditions/lung- cancer/.

[2]. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. 2016. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5), 1299-1312.

[3]. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. (LeNet)

[4]. Simonyan, K., and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. (VGG)

[5]. He, K., Zhang, X., Ren, S., and Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). (ResNet)

[6]. Al-Shabi, M., Shak, K., and Tan, M. 2022. ProCAN: Progressive growing channel attentive non- local network for lung nodule classification. Pattern Recognition, 122, 108309.

[7]. Armato III, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., ... and Clarke, L. P. 2011. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical physics, 38(2), 915-931.

[8]. Zhu, W., Liu, C., Fan, W., and Xie, X. 2018. Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. In 2018 IEEE winter conference on applications of computer vision (WACV) (pp. 673-681). IEEE. (DeepLung)

[9]. Al-Shabi, M., Lan, B. L., Chan, W. Y., Ng, K. H., and Tan, M. 2019. Lung nodule classification using deep local–global networks. International journal of computer assisted radiology and surgery, 14(10), 1815-1819. (Local-Global)

[10]. Beini, Z., Xuee, C., Bo, L., and Weijia, W. 2021. A new few-shot learning method of digital PCR image detection. IEEE Access, 9, 74446-74453.

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

[12]. Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. 2019. Self-attention generative adversarial networks. In International conference on machine learning (pp. 7354-7363). PMLR. (selfattention)

[13]. Karras, T., Aila, T., Laine, S., and Lehtinen, J. 2017. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196. (ProGAN)

[14]. Xie, Y., Xia, Y., Zhang, J., Song, Y., Feng, D., Fulham, M., and Cai, W. 2018. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE transactions on medical imaging, 38(4), 991-1004. (MV-KBC)

[15]. Xie, Y., Zhang, J., and Xia, Y. 2019. Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT. Medical image analysis, 57, 237-248. (MV- SSAC)

[16]. Al-Shabi, M., Lee, H. K., and Tan, M. 2019. Gated-dilated networks for lung nodule classification in CT scans. IEEE Access, 7, 178827-178838.

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