Skip to main content

Advertisement

SpringerLink
Log in

Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest challenge in medical image classification with the help of CNN is the difficulty of acquiring enough samples, and overfitting is a common problem when there are not enough images for training. Transfer learning has been verified as reasonable in dealing with such problems with an acceptable loss value. We use the classic LeNet-5 model to classify pulmonary nodules of thoracic CT images, including benign and malignant pulmonary nodules, and different malignancies of the malignant nodules. The CT images are obtained from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) where both pulmonary nodule scanning and nodule annotations are available. These images are labeled and stored in a medical images knowledge base (KB), which is designed and implemented in our previous work. We implement the 10-folder cross validation (CV) to testify the robustness of the classification model we trained. The result demonstrates that the transfer learning of the LeNet-5 is good for classifying pulmonary nodules of thoracic CT images, and the average values of Top-1 accuracy are 97.041% and 96.685% respectively. We believe that our work is beneficial and has potential for practical diagnosis of lung nodules.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Hubel DH, Wiesel TN: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160 (1): 106–154, 1962

    Article  CAS  Google Scholar 

  2. Deng J, Dong W, Socher R, Li JL, Li K, Li FF: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on computer vision and pattern recognition, 2009, pp 248–255

  3. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein MS, Berg AC, Li F-F: Imagenet large scale visual recognition challenge. International Journal of Computer Vision . arXiv:http://arXiv.org/abs/1409.0575, 2014

  4. Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks.. In: Advances in neural information processing systems, 2012, pp 1097–1105

  5. Krizhevsky A, Hinton G: Learning multiple layers of features from tiny images, 2009

  6. Tran PV A fully convolutional neural network for cardiac segmentation in short-axis mri, arXiv:http://arXiv.org/abs/1604.00494, 2016

  7. Ng H-W, Nguyen VD, Vonikakis V, Winkler S: Deep learning for emotion recognition on small datasets using transfer learning. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. ACM, 2015, pp 443–449

  8. Raina R, Battle A, Lee H, Packer B, Ng AY: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning. ACM, 2007, pp 759–766

  9. Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T Decaf: A deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, 2014, pp 647–655

  10. LeCun Y, Bottou L, Bengio Y, Haffner P: Gradient-based learning applied to document recognition. Proc IEEE 86 (11): 2278–2324, 1998

    Article  Google Scholar 

  11. Hu W, Huang Y, Li W, Zhang F, Li H: Deep convolutional neural networks for hyperspectral image classification. J Sensors, 2015

  12. Sarraf S, Tofighi G: Deep learning-based pipeline to recognize alzheimer’s disease using fmri data. In: Future Technologies Conference, 2017

  13. Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, ..., Kazerooni EA: 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, 2011

    Article  Google Scholar 

  14. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick RB, Guadarrama S, Darrell T: Caffe: convolutional architecture for fast feature embedding. ACM Multimed, arXiv:http://arXiv.org/abs/1408.5093, 2014, 675–678

  15. Shen W, Mu Z, Yang F, Yang C, Tian J: Multi-scale convolutional neural networks for lung nodule classification. In: International Conference on Information Processing in Medical Imaging. Springer, 2015, pp 588–599

  16. Song Q, Zhao L, Luo X, Dou X: Using deep learning for classification of lung nodules on computed tomography images. Journal of Healthcare Engineering, 2017

  17. Krewer H, Geiger B, Hall LO, Goldgof DB, Gu Y, Tockman M, Gillies RJ: Effect of texture features in computer aided diagnosis of pulmonary nodules in low-dose computed tomography. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2013, pp 3887–3891

  18. Golan R, Jacob C, Denzinger J: Lung nodule detection in ct images using deep convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016, pp 243–250

  19. Bergtholdt M, Wiemker R, Klinder T: Pulmonary nodule detection using a cascaded svm classifier. In: Medical Imaging 2016: Computer-Aided Diagnosis. International Society for Optics and Photonics, 2016, vol 9785, pp 978513

  20. Zhang C, Sun F, Zhang M, Liu W, Yu Q, Babyn P, Zhong H: Design and implementation of a medical image knowledge base for pulmonary nodules diagnosis. In: IEEE International Conference on Computer and Communications, 2018, pp 2071– 2075

  21. Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M: Medical image classification with convolutional neural network, 2014, pp 844–848

  22. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM: Deep convolutional neural networks for computer-aided detection Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35 (5): 1285, 2016

    Article  Google Scholar 

  23. Kuncheva LI, Juan J: . Rodriguez Classifier ensembles with a random linear oracle 19: 500–508, 2007

    Google Scholar 

  24. Baralis E, Chiusano S, Garza P: A lazy approach to associative classification. IEEE Trans Knowl Data Eng 20 (2): 156–171, 2008

    Article  Google Scholar 

  25. Zhou X: Semi - supervised learning literature survey, 2005

  26. Nigam K, McCallum AK, Thrun S, Mitchell T: Text classification from labeled and unlabeled documents using em. Mach Learn 39: 103–134, 2000

    Article  Google Scholar 

  27. Blum A, Mitchell T: Combining labeled and unlabeled data with co-training, 1998, pp 92–100

  28. Joachims T: Transductive inference for text classification using support vector machines. In: International Conference on Machine Learning, 1999, pp 200–209

  29. Pan SJ, Yang Q: A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10): 1345–1359, 2010

    Article  Google Scholar 

  30. Lawrence ND, Platt JC: Learning to learn with the informative vector machine. In: International Conference on Machine Learning, 2004, pp 65–65

  31. Bonilla EV, Chai KM, Williams C: Multi-task gaussian process prediction. Annual Conference on Neural Information Processing Systems, 2007

  32. Schwaighofer A, Tresp V, Yu K: Learning gaussian process kernels via hierarchical Bayes. Annual Conference on Neural Information Processing Systems, 2004

  33. Evgeniou T, Pontil M: Regularized multi–task learning, 2004, pp 109–117

  34. Gao J, Fan W, Jiang J, Han J: Knowledge transfer via multiple model local structure mapping. ACM Knowl Discov Data Mining, 283–291, 2008

  35. Glorot X, Bengio Y: Understanding the difficulty of training deep feedforward neural networks 9,249–256, 2010

  36. Wanqing C, Rongshou Z, Baade PD, Siwei Z, Hongmei Z, Freddie B, Ahmedin J, Qin YX, Jie H: Cancer statistics in China, 2015, 2016

  37. Siegel R, Ma J, Zou Z, Jemal A: Cancer statistics, 2014. CA: Cancer J Clin 64 (1): 9–29, 2014

    Google Scholar 

  38. Henschke CI: Early lung cancer action project: overall design and findings from baseline screening 89, 2474–2482, 2000

Download references

Funding

This work is supported by the Nature Science Foundation of Shandong Province under the grant ZR2014FM006, the National Nature Science Foundation of China under the grant 81671703, and the Focus on Research and Development Plan in Shandong Province under the grant 2015GSF118026.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong University, Jinan, China

    Shikun Zhang, Fengrong Sun, Cuicui Zhang, Qianlei Yu & Mingqiang Zhang

  2. Orthopedics Department, Taian Traditional Chinese Medicine Hospital, Taian, China

    Naishun Wang

  3. Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, Canada

    Paul Babyn

  4. Radiology Department, The Second Hospital of Shandong University, Jinan, China

    Hai Zhong

Authors
  1. Shikun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fengrong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naishun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cuicui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qianlei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mingqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paul Babyn
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hai Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fengrong Sun.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

We talked about transfer learning using CNN, trained on LIDC-IDRI database; although LeNet-5 was chosen as the CNN model in the main content, we also evaluated AlexNet, which is newer than the oldest LeNet architecture. The results including sensitivity, specificity, and TOP-1 accuracy as well as ROC and AUC are shown below in Tables 7 and 8, and Figs. 15 and 16.

Table 7 Malignant-nodule and non-nodule classification result of AlexNet
Full size table
Table 8 Serious-Malignant and Mild-Malignant classification result of AlexNet
Full size table
Fig. 15
figure 15

Malignant-nodule–non-nodule ROC curve with AUC value of AlexNet

Full size image
Fig. 16
figure 16

Serious-Malignant–Mild-Malignant ROC curve with AUC value of AlexNet

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Sun, F., Wang, N. et al. Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning. J Digit Imaging 32, 995–1007 (2019). https://doi.org/10.1007/s10278-019-00204-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-019-00204-4

Keywords

Search

Navigation