NoduleNet: A Lung Nodule Classification Using Deep Learning

Main Article Content

Ganesh Shah
Ratchainant Thammasudjarit
Ammarin Thakkinstian
Thitiporn Suwatanapongched

Abstract

Background: Accurate detection and classification of lung nodules at an early stage can help physicians to improve the treatment outcomes of lung cancer. Several lung nodule classifications using deep learning have been proposed but they are lag of external validation to Thai patient data.


Objective: To propose a deep learning model called NoduleNet for lung nodule classification and perform internal and external validation of the proposed model.


Methods: Two datasets were performed; internal validation using LUNA16 (the public lung CT database), and external validation using ChestRama (37 chest CT scans retrospectively identified from the CT database of Ramathibodi Hospital between 2017 and 2019). The NoduleNet was built on top of pretrained architecture, VGG16, and VGG19 with customization.


Results: The NoduleNet showed impressive results in nodule classification. The best model achieved accuracy of 0.95 (0.94 - 0.96), sensitivity of 0.84 (0.82 - 0.86), and specificity of 0.97 (0.97 - 0.98) for internal validation, where the external validation results was accuracy of 0.95 (0.87 - 1.00), sensitivity of 0.91 (0.82 - 1.00), and specificity of 1.00 (1.00 - 1.00). There were 3 misclassified samples in external validation which are all false-negative.


Conclusions: The NoduleNet is able to generalize from non-Thai patient data to Thai patient data. It could be further improved by taking sequence of images into account, integrating with an automatic nodule detection algorithm, and adding more nodule types.


 

Article Details

How to Cite
Shah, G., Thammasudjarit, R., Thakkinstian, A., & Suwatanapongched, T. (2020). NoduleNet: A Lung Nodule Classification Using Deep Learning. Ramathibodi Medical Journal, 43(4), 11–19. https://doi.org/10.33165/rmj.2020.43.4.241727
Section
Original Articles

References

International Agency for Research on Cancer, World Health Organization. Cancer fact sheets: all cancers. https://gco.iarc.fr/today/data/factsheets/cancers/39-All-cancers-fact-sheet.pdf. Accessed May 13, 2020.

Al Mohammad B, Hillis SL, Reed W, Alakhras M, Brennan PC. Radiologist performance in the detection of lung cancer using CT. Clin Radiol. 2019;74(1):67-75. doi:10.1016/j.crad.2018.10.008.

Nair A, Screaton NJ, Holemans JA, et al. The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial. Eur Radiol. 2018;28(1):226-234. doi:10.1007/s00330-017-4903-z.

Jiang H, Ma H, Qian W, Wei G, Zhao X, Gao M. A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2017:556-559. doi:10.1109/EMBC.2017.8036885.

El-Baz A, Nitzken M, Vanbogaert E, Gimel’Farb G, Falk R, Abo El-Ghar M. A novel shape-based diagnostic approach for early diagnosis of lung nodules. Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2011:137-140. doi:10.1109/ISBI.2011.5872373.

McNitt-Gray MF, Hart EM, Wyckoff N, Sayre JW, Goldin JG, Aberle DR. A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys. 1999;26(6):880-888. doi:10.1118/1.598603.

Mukherjee J, Chakrabarti A, Shaikh SH, Kar M. Automatic detection and classification of solitary pulmonary nodules from lung CT images. Proceedings of the International Conference on Emerging Applications of Information Technology. 2014:294-299. doi:10.1109/EAIT.2014.64.

Iwano S, Nakamura T, Kamioka Y, Ishigaki T. Computer-aided diagnosis: A shape classification of pulmonary nodules imaged by high-resolution CT. Comput Med Imaging Graph. 2005;29(7):565-570. doi:10.1016/j.compmedimag.2005.04.009.

Fernandes VPM, Kanehisa RFA, Braz G, Silva AC, de Paiva AC. Lung nodule classification based on shape distributions. Proceedings of the ACM Symposium on Applied Computing. 2016:84-86. doi:10.1145/2851613.2851877.

Xie Y, Zhang J, Liu S, Cai W, Xia Y. Lung nodule classification by jointly using visual descriptors and deep features. In: Muller H, Kelm BM, Arbel T, eds. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. Athens, Greece: Springer-VDI-Verlag GmbH & Co. KG; 2017:116-125. doi:10.1007/978-3-319-61188-4_11.

Song J, Liu H, Geng F, Zhang C. Weakly-supervised classification of pulmonary nodules based on shape characters. Proceedings of the 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress. 2016:228-232. doi:10.1109/DASC-PICom-DataCom-CyberSciTec.2016.58.

Al-Saffar AAM, Tao H, Talab MA. Review of deep convolution neural network in image classification. Proceeding of the International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications. 2017:26-31. doi:10.1109/ICRAMET.2017.8253139.

He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M. Bag of tricks for image classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019:558-567. doi:10.1109/CVPR.2019.00065.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations; 2015. https://arxiv.org/pdf/1409.1556.pdf. Accessed May 19, 2020.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778. doi:10.1109/CVPR.2016.90.

Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9. doi:10.1109/CVPR.2015.7298594.

Chollet F. Xception: deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:1800-1807. doi:10.1109/CVPR.2017.195.

Howard AG, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. https://arxiv.org/abs/1704.04861v1. Accessed May 19, 2020.

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2261-2269. doi:10.1109/CVPR.2017.243.

Zoph B, Vasudevan V, Shlens J, Le QV. Learning transferable architectures for scalable image recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:8697-8710. doi:10.1109/CVPR.2018.00907.

Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211-252. doi:10.1007/s11263-015-0816-y.

Setio AAA, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal. 2017;42:1-13. doi:10.1016/j.media.2017.06.015.

Armato SG 3rd, McLennan G, Bidaut L, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011;38(2):915-931. doi:10.1118/1.3528204.

Sajja TK, Devarapalli RM, Kalluri HK. Lung cancer detection based on CT scan images by using deep transfer learning. Trait Signal. 2019;36(4):339-344. doi:10.18280/ts.360406.