Journal of Digital Imaging

, Volume 32, Issue 6, pp 971–979 | Cite as

Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network

  • Qin Wang
  • Fengyi Shen
  • Linyao Shen
  • Jia Huang
  • Weiguang ShengEmail author


Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CAD) system using CNNs and CT image segmentation techniques. Unlike former studies focusing on the classification of malignant nodule types or relying on prior image processing, in this work, we put raw CT image patches directly in CNNs to reduce the complexity of the system. Specifically, we split each CT image into several patches, which are divided into 6 types consisting of 3 nodule types and 3 non-nodule types. We compare the performance of ResNet with different CNNs architectures on CT images from a publicly available dataset named the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). Results show that our best model reaches a high detection sensitivity of 92.8% with 8 false positives per scan (FPs/scan). Compared with related work, our work obtains a state-of-the-art effect.


Deep learning Convolutional neural network Computer-aided detection Lung nodule detection 



This work is supported in part by National Natural Science Foundation of China (61772331). We would also like to thank the Shanghai Chest Hospital and department of micro/nanoelectronics at Shanghai Jiao Tong University.

Supplementary material

10278_2019_221_MOESM1_ESM.hdf5 (49.3 mb)
ESM 1 (HDF5 50,485 kb)


  1. 1.
    Siegel RL, Miller KD, Jemal A: Cancer statistics, 2015. CA Cancer J Clin 65(1):5–29, 2015CrossRefGoogle Scholar
  2. 2.
    Ma L, Wang DD, Zou B, Yan H: An eigen-binding site based method for the analysis of anti-EGFR drug resistance in lung cancer treatment. IEEE/ACM Trans Comput Biol Bioinform 14(5):1187–1194, 2017CrossRefGoogle Scholar
  3. 3.
    Kaneko M, Eguchi K, Ohmatsu H, Kakinuma R, Naruke T, Suemasu K, Moriyama N: Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 201(3):798–802, 1996CrossRefGoogle Scholar
  4. 4.
    Jiang H, Ma H, Qian W et al.: An automatic detection system of lung nodule based on multi-group patch-based deep learning network. IEEE J Biomed Health Inform 22:1227–1237, 2017CrossRefGoogle Scholar
  5. 5.
    Messay T, Hardie RC, Rogers SK: A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3):390–406, 2010CrossRefGoogle Scholar
  6. 6.
    Gurcan MN, Sahiner B, Petrick N, Chan HP, Kazerooni EA, Cascade PN, Hadjiiski L: Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system. Med Phys 29(11):2552–2558, 2002CrossRefGoogle Scholar
  7. 7.
    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. Nat Commun 5:4006, 2014CrossRefGoogle Scholar
  8. 8.
    Balagurunathan Y, Gu Y, Wang H, Kumar V, Grove O, Hawkins S, Kim J, Goldgof DB, Hall LO, Gatenby RA, Gillies RJ: Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol 7(1):72–87, 2014CrossRefGoogle Scholar
  9. 9.
    Rendon-Gonzalez E, Ponomaryov V: Automatic lung nodule segmentation and classification in CT images based on SVM. International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves. IEEE 1–4, 2016Google Scholar
  10. 10.
    Lee S L A, Kouzani A Z, Hu E J: A random forest for lung nodule identification. TENCON 2008 - 2008 IEEE Region 10 Conference. IEEE:1–5, 2008Google Scholar
  11. 11.
    Adetiba E, Olugbara OO: Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features. ScientificWorldJourna 2015:1–17, 2015. CrossRefGoogle Scholar
  12. 12.
    Shan C: Learning local binary patterns for gender classification on real-world face images. Pattern Recognit Lett 33(4):431–437, 2012CrossRefGoogle Scholar
  13. 13.
    Orozco HM, Villegas OOV, Sánchez VGC, Domínguez HJO, Alfaro MJN: Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed Eng Online 14(1):9, 2015CrossRefGoogle Scholar
  14. 14.
    Tartar A, Akan A, Kilic N: A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers. Conf Proc IEEE Eng Med Biol Soc: 4651–4654, 2014Google Scholar
  15. 15.
    Kang G, Liu K, Hou B, Zhang N: 3D multi-view convolutional neural networks for lung nodule classification. PLoS One 12:e0188290, 2017. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Han F, Wang H, Zhang G, Han H, Song B, Li L, Moore W, Lu H, Zhao H, Liang Z: Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging 28(1):99–115, 2015CrossRefGoogle Scholar
  17. 17.
    Hinton GE, Osindero S, Teh YW: A fast learning algorithm for deep belief nets. Neural Comput, 2006. CrossRefGoogle Scholar
  18. 18.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A: Going deeper with convolutions. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 2015.Google Scholar
  19. 19.
    Ren S, He K, Girshick R, Sun J: Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst: 91–99, 2015Google Scholar
  20. 20.
    Liang-Chieh C, Papandreou G, Kokkinos I, Murphy K, Yuille A: Semantic image segmentation with deep convolutional nets and fully connected crfs. International Conference on Learning Representations, 2015Google Scholar
  21. 21.
    Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst:1097–1105, 2012Google Scholar
  22. 22.
    Simonyan K, Zisserman A: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014Google Scholar
  23. 23.
    He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 770–778, 2016Google Scholar
  24. 24.
    Golan R, Jacob C, Denzinger J: Lung nodule detection in CT images using deep convolutional neural networks. International Joint Conference on Neural Networks: 243–250. 2016Google Scholar
  25. 25.
    Wang S, Zhou M, Gevaert O, Tang Z, Dong D, Liu Z, Tian J: A multi-view deep convolutional neural networks for lung nodule segmentation. Conf Proc IEEE Eng Med Biol Soc 2017, 1752–1755Google Scholar
  26. 26.
    Liu K, Kang G: Multiview convolutional neural networks for lung nodule classification. Int J Imaging Syst Technol, DOI:, 2017CrossRefGoogle Scholar
  27. 27.
    Huang X, Shan J, Vaidya V: Lung nodule detection in CT using 3D convolutional neural networks. 2017 IEEE 14th International Symposium on Biomedical Imaging 379–383, 2017Google Scholar
  28. 28.
    Gu Y, Lu X, Yang L, Zhang B, Yu D, Zhao Y, Gao L, Wu L, Zhou T: Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput. Biol. Med. 103:220–231, 2018CrossRefGoogle Scholar
  29. 29.
    Zhu W, Liu C, Fan W, Xie X: Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. 2018 IEEE Winter Conf. Appl. Comput. Vision, WACV: 673-681, 2018Google Scholar
  30. 30.
    Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, 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, 2011CrossRefGoogle Scholar
  31. 31.
    Van Ginneken B, Setio AA, Jacobs C, Ciompi F: Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. IEEE Int. Symp. Biomed. Imaging:286–289, 2015Google Scholar
  32. 32.
    Han F, Zhang G, Wang H, et al: A texture feature analysis for diagnosis of pulmonary nodules using LIDC-IDRI database. IEEE International Conference on Medical Imaging Physics and Engineering, 2014, 14–18.Google Scholar
  33. 33.
    Htwe K Z, Yamamori K, Katayama T, Kyi T M: Automated lung nodule classification by artificial neural network and fuzzy inference system. Consumer Electronics, 2016 IEEE, Global Conference on IEEE, 2016 1–2.Google Scholar
  34. 34.
    Van Ginneken B, Armato SG, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME, Camarlinghi N: Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal 14(6):707–722, 2011CrossRefGoogle Scholar
  35. 35.
    Fawcett T: An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874, 2006CrossRefGoogle Scholar
  36. 36.
    Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, van Ginneken B: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169, 2016CrossRefGoogle Scholar
  37. 37.
    Jiang H, Ma H, Qian W, Gao M, Li Y: An automatic detection system of lung nodule based on multi-group patch-based deep learning network. IEEE J Biomed Health Inform (99):1–1, 2017Google Scholar
  38. 38.
    Khosravan N, Bagci U: S4ND: Single-shot single-scale lung nodule detection. Med Image Comput Comput Assist Interv:794–802, 2018Google Scholar
  39. 39.
    Broyelle A: Automated Pulmonary Nodule Detection on Computed Tomography Images with 3D Deep Convolutional Neural Network, School of Computer Science and Communication KTHRoyal Institute of Technology, Stockholm 2018.Google Scholar
  40. 40.
    Dou Q, Chen H, Jin Y, Lin H, Qin J, Heng PA: Automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid-loss residual learning. Med Image Comput Comput Assist Interv:630–638, 2017Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  • Qin Wang
    • 1
  • Fengyi Shen
    • 1
  • Linyao Shen
    • 1
  • Jia Huang
    • 2
  • Weiguang Sheng
    • 1
    Email author
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Chest HospitalShanghaiChina

Personalised recommendations