Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network

AUTHORS

Muhammad Bilal Zia,Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
Juan Juan Zhao,Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China
Xiao Ning,Graduate school of Information and Computer Science, Taiyuan University of Technology, Taiyuan, China

ABSTRACT

Lung nodule classification has been one of the major problem relevant to Computer-Aided Diagnosis (CAD) system. Lung cancer for both men and women has been one of the leading causes of cancer related death. Deep learning models have produced promising performance in recent years, outperforming traditional methods in different fields. Nowadays, scientists have attempted numerous deep learning approaches to enhance the efficiency of CAD systems via Computed Tomography (CT) in lung cancer screening. In this paper, we presented a completely automatic lung CT system for cancer diagnosis named Two-step Deep Network (TsDN) and it contains two parts detection of nodule and classification. First, Improved 3D-Faster R-CNN with U-net like encoder and decoder is used for detection of nodule and then Multi-scale Multi-crop Convolutional Neural Network (MsMc-CNN) is proposed for the pulmonary nodule classification. The multi scale approach uses filters of various sizes to extract nodule features more efficiently from the local regions, and then multi crop pooling technique involves in extracting the important nodule information that cultivates various regions from convolutional feature map and then add numerous times for the maximum pooling. The proposed TsDN is trained and evaluated on LIDC-IDRI public dataset and achieved a sensitivity of 0.885 and specificity of 0.922 with AUC of 0.946.

 

KEYWORDS

Detection, Classification, 3D-Faster R-CNN, Multi-scale Multi-crop CNN

REFERENCES

[1]     Siegel R., Ma J., Zou Z., and Jemal A., “Cancer statistics,” CA: A Cancer Journal for Clinicians, vol.64, pp.9-29, (2014)
[2]     Atwater, Thomas, Christine M. Cook, and Pierre P. Massion. “The pursuit of noninvasive diagnosis of lung cancer,” Seminars in respiratory and critical care medicine, vol.37, no.05, Thieme Medical Publishers, (2016)
[3]     Aberle, D. R. “Implementing lung cancer screening: the US experience,” Clinical radiology, vol.72, no.5, pp.401-406, (2017)
[4]     National Lung Screening Trial Research Team, “Reduced lung-cancer mortality with low-dose computed tomographic screening,” New England Journal of Medicine, vol.365, no.5, pp.395-409, (2011)
[5]     Siegel, Rebecca L., Kimberly D. Miller, and Ahmedin Jemal, “Cancer statistics, 2018,” CA: a cancer journal for clinicians, vol.68, no.1, pp.7-30, (2018)
[6]     Van beek E. J., Mirsadraee S., and Murchison J. T., Lung cancer screening: Computed tomography or chest radiographs? World Journal of Radiology, vol.7, no.8, pp.189-193, (2015)
[7]     Zhang Y., Oikonomou A., Wong A., Haider M. A., and Khalvati F, “Radiomics-based prognosis analysis for non-small cell lung cancer,” Scientific reports, vol.7, pp.463-469, (2017)
[8]     Causey J. L., Zhang J., Ma S., Jiang B., Qualls J. A., Politte D. G., and Huang X. “Highly accurate model for prediction of lung nodule malignancy with CT scans,” Scientific reports, vol.8, no.1, pp.1-12, (2018)
[9]     Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nature communications, vol.5, no.1, pp.1-9, (2014)
[10]  Oikonomou and Anastasia, et al. “Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy,” Scientific reports, vol.8, no.1, pp.1-11, (2018)
[11]  Afshar A., Mohammadi A., Konstantinos N. P., Oikonomou A. and Benali H. “From hand-crafed to deep learning-based cancer radiomics: Challenges and opportunities,” IEEE Signal. Process, Mag, vol.36, pp.132-160
[12]  Gillies, Robert J., Paul E. Kinahan, and Hedvig Hricak, “Radiomics: images are more than pictures, they are data,” Radiology, vol.278, no.2, pp.563-577, (2016)
[13]  Lambin, P. et al. “Radiomics: extracting more information from medical images using advanced feature analysis,” Eur. J. Cancer, vol.48, pp.441-446, (2012)
[14]  Chen Chia-Hung, et al. “Radiomic features analysis in computed tomography images of lung nodule classification,” PloS one, vol.13, no.2, e0192002, (2018)
[15]  Parmar Chintan, et al. “Radiomic feature clusters and prognostic signatures specific for lung and head and neck cancer,” Scientific reports, vol.5, 11044, (2015)
[16]  Yip Stephen SF and Hugo JWL Aerts. “Applications and limitations of radiomics,” Physics in Medicine and Biology, vol.61, no.13, R150, (2016)
[17]  Silver D., Huang A., Maddison C. J., Guez A., Sifre L., van den Driessche, G., et al. “Mastering the game of Go with deep neural networks and tree search,” Nature, vol.529, pp.484-489, (2016)
[18]  Szegedy C., Vanhoucke V., Ioffe S., Shlens J., and Wojna Z., Rethinking the inception architecture for computer vision, CoRR, (2015)
[19]  Tajbakhsh N., Shin J. Y., Gurudu S. R., Hurst R. T., Kendall C. B., and Gotway M. B., et al. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, vol.35, no.5, pp.1299-1312, (2016)
[20]  Zhao X., Liu L., Qi S., Teng Y., Li J., and Qian W. Agile convolutional neural network for pulmonary nodule classification using CT images. International Journal of Computer Assisted Radiology and Surgery, vol.13, no.4, pp.585-595, (2018)
[21]  Park J. E. et al. “Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives,” Korean J. Radiology 20, pp.1124-1137,
[22]  K. Murphy et al. “A large-scale evaluation of automatic pulmonary nodule detection in chest ct using local image features and k-nearest-neighbour classification,” Medical image analysis, (2009)
[23]  S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” In NIPS, (2015)
[24]  J. Ding, A. Li, Z. Hu, and L. Wang, “Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks,” In MICCAI, (2017)
[25]  O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” In MICCAI, (2015)
[26]  S. G. Armato et al. “The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans,” Medical physics, vol.38, no.2, pp.915-931, (2011)
[27]  Bishop C. M., Pattern recognition and machine learning (information science and statistics), Secaucus, NJ: Springer, (2006)
[28]  Srivastava N., Hinton G., Krizhevsky A., Sutskever I., and Salakhutdinov R., Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, vol.15, no.1, pp.1929-1958, (2014)
[29]  K. He, X. Zhang, S. Ren, and J. Sun., “Deep residual learning for image recognition,” In CVPR, (2016)
[30]  X., Liu, L., Qi, S., Teng, Y., Li, J., and Qian, W., “Agile convolutional neural network for pulmonary nodule classification using CT images,” International Journal of Computer Assisted Radiology and Surgery, vol.13, no.4, 585-595, (2018)
[31]  Zhao D., Zhu D., Lu J., Luo Y., and Zhang G., “Synthetic medical images using Fand BGAN for improved lung nodules classification by multi-scale VGG16,” Symmetry, vol.10, no.10, 519, (2018)
[32]  Tafti A. P., Bashiri F. S., LaRose E., and Peissig P., “Diagnostic classification of lung CT images using deep 3d multi-scale convolutional neural network,” In 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp.412-414, IEEE, (2018)
[33]  Gu Y., Lu X., Yang L., Zhang B., Yu D., Zhao Y., and Zhou T., “Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs,” Computers in biology and medicine, vol.103, no.220-231, (2018)
[34]  Shen W., Zhou M., Yang F., Yang C., and Tian J., “Multi-scale convolutional neural networks for lung nodule classification,” In International Conference on Information Processing in Medical Imaging, pp.588-599, Springer, Cham, (2015)

CITATION

  • APA:
    Zia,M.B.& Zhao,J.J.& Ning,X.(2020). Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network. International Journal of Hybrid Information Technology, 13(2), 45-56. 10.21742/IJHIT.2020.13.2.04
  • Harvard:
    Zia,M.B., Zhao,J.J., Ning,X.(2020). "Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network". International Journal of Hybrid Information Technology, 13(2), pp.45-56. doi:10.21742/IJHIT.2020.13.2.04
  • IEEE:
    [1] M.B.Zia, J.J.Zhao, X.Ning, "Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network". International Journal of Hybrid Information Technology, vol.13, no.2, pp.45-56, Sep. 2020
  • MLA:
    Zia Muhammad Bilal, Zhao Juan Juan and Ning Xiao. "Detection and Classification of Lung Nodule in Diagnostic CT: A TsDN method based on Improved 3D-Faster R-CNN and Multi-Scale Multi-Crop Convolutional Neural Network". International Journal of Hybrid Information Technology, vol.13, no.2, Sep. 2020, pp.45-56, doi:10.21742/IJHIT.2020.13.2.04
 

COPYRIGHT

Creative Commons License
© 2020 Muhammad Bilal Zia et al. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ISSUE INFO

  • Volume 13, No. 2, 2020
  • ISSN(p):1738-9968
  • ISSN(e):2652-2233
  • Published:Sep. 2020

DOWNLOAD