Agile convolutional neural network for pulmonary nodule classification using CT images

  • Xinzhuo Zhao
  • Liyao Liu
  • Shouliang Qi
  • Yueyang Teng
  • Jianhua Li
  • Wei Qian
Original Article



To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images.


A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally.


After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to \(7\times 7\), the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used.


This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.


Lung cancer Nodule classification Deep learning Convolutional neural network 



The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health and their critical role in the creation of the free publicly available LIDC-IDRI Database used in this study.


This study was funded by the National Natural Science Foundation of China under Grant (Nos. 81671773, 61672146).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in these 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. For this type of study, formal consent is not required.


  1. 1.
    Siegel R, Naishadham D, Jemal A (2013) Cancer statistics. CA-Cancer J Clin 63(1):11–30. CrossRefPubMedGoogle Scholar
  2. 2.
    Chen D, Zheng R, Peter D, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu X, He J (2015) Cancer statistics in China. CA-Cancer J Clin 66(2):115–132. CrossRefGoogle Scholar
  3. 3.
    Valente IR, Cortez PC, Neto EC, Soares JM, De Albuquerque VH, Tavares JM (2016) Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods Prog Biomed 124(C):91–107. CrossRefGoogle Scholar
  4. 4.
    Gridelli C, Rossi A, Carbone DP, Guarize J, Karachaliou N, Mok T, Petrella F, Spaggiari L, Rosell R (2015) Non-small-cell lung cancer. Nat Rev Dis Primers 2(T3):N1. Google Scholar
  5. 5.
    Elbaz A, Beache GM, Gimelfarb G, Suzuki K, Okada K, Elnakib A, Soliman A, Abdollahi B (2013) Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging 1:942353–942353. Google Scholar
  6. 6.
    Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imag Gr 31(4–5):198–211. CrossRefGoogle Scholar
  7. 7.
    Parmar C, Grossmann P, Bussink J, Lambin P, AertsH J (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278(2):563. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Greenspan H, van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35(5):1153–1159. CrossRefGoogle Scholar
  10. 10.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. CrossRefPubMedGoogle Scholar
  11. 11.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. CrossRefGoogle Scholar
  12. 12.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS 2012, pp 1097–1105Google Scholar
  13. 13.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci. arXiv:1409.1556
  14. 14.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: CVPR 2015, pp 1–9.
  15. 15.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770–778Google Scholar
  16. 16.
    Hua KL, Hsu CH, Hidayati SC, Cheng W, Chen Y (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 8:2015–2022. PubMedPubMedCentralGoogle Scholar
  17. 17.
    Sun W, Zheng B, Qian W (2016) Computer aided lung cancer diagnosis with deep learning algorithms. In: SPIE Medical Imaging 9785 2016:97850Z-97850Z-8.
  18. 18.
    Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Chen CM (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in CT scans. Sci Rep 6:24454. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Kumar D, Wong A, Clausi DA (2015) Lung nodule classification using deep features in CT images. Comput Robot Vis 2015:133–138. Google Scholar
  20. 20.
    Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification. IPIM 2015:588–599. Google Scholar
  21. 21.
    Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Zang Y, Tian J (2017) Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 61:663–673. CrossRefGoogle Scholar
  22. 22.
    Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP (2015) Data from LIDC-IDRI. The Cancer Imaging Archive.
  23. 23.
    Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beeke EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY (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. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore SM, Phillips S, Maffitt DR, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Sun W, Tseng TLB, Zhang J, Qian W (2016) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Gr 57:4–9. CrossRefGoogle Scholar
  26. 26.
    Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Lopez MAG (2016) Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed 127:248–257. CrossRefPubMedGoogle Scholar
  27. 27.
    Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines (ICML-10), pp 807–814Google Scholar
  28. 28.
    Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. ACMMM 2014:675–678. Google Scholar
  29. 29.
    Srivastava N (2013) Improving neural networks with dropout. University of Toronto. Accessed 18 Feb 2013
  30. 30.
    Keskar NS, Mudigere D, Nocedal J, Smelyanskiy M, Tang PTP (2016) On large-batch training for deep learning: generalization gap and sharp minima. arXiv:1609.04836
  31. 31.
    Setio AAA, Ciompi F, Litjens G, Gerke PK, Jacobs C, van Riel S, Wille MMW, Naqibullah M, Sanchez CI, van Ginneken B (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169. CrossRefPubMedGoogle Scholar
  32. 32.
    Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298. CrossRefPubMedGoogle Scholar
  33. 33.
    Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangChina
  2. 2.College of Engineering, University of Texas at El PasoEl PasoUSA

Personalised recommendations