ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

Anna A. Meldo, Lev V. Utkin A review of the intelligent lung cancer diagnosis methods


An overview of new lung cancer intellectual diagnosis methods and of peculiarities of intelligent diagnostic systems that classify lung nodules in images obtained from the computed tomography is presented in the paper. Basic steps of the lung nodule detection and classification are considered. The main attention is paid to the analysis of the diagnostic system intellectual components based on deep and shallow learning algorithms.


artificial intelligence, lung cancer, classification, computed tomography, diagnostic system.

PP. 28-38.

DOI 10.14357/20718594180313


1. Doi K. Current status and future potential of computer-aided diagnosis in medical imaging. The British Journal of Radiology, 78:s3-s19, 2005.
2. Firmino M., Morais A.H., Mendoca R.M., Dantas M.R., Hekis H.R., Valentim R. Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomedical engineering online, 13(1):41, 2014.
3. Rehman M.Z., Javaid M., Shah S.I.A., Gilani S.O., Jamil M., Butt S.I. An appraisal of nodules detection techniques for lung cancer in ct images. Biomedical Signal Processing and Control, 41:140-151, 2018.
4. Wu H., Yao M., Hu A., Sun G., Yu X., Tang J. A systematic analysis for state-of-the-art 3D lung nodule proposals generation. arXiv:1802.02179, Jan 2018.
5. Choi W.J., Choi T.S. Automated pulmonary nodule detection based on three dimensional shape-based feature descriptor. Computer Methods and Programs in Biomedicine, 113:37-54, 2014.
6. Shaukat F., Raja G., Gooya A., Frangi A.F. Fully automatic detection of lung nodules in CT images using a hybrid feature set. Medical Physics, 44(7):3615-3629, Jul 2017.
7. Kostis W.J., Reeves A.P., Yankelevitz D.F., Henschke C.I. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Transactions on Medical Imaging, 22(10):1259-1274, 2003.
8. Chon A., Balachandar N., Lu P. Deep convolutional neural networks for lung cancer detection. Technical report, Stanford University, 2017.
9. Ronneberger O., Fischer P., Brox T. U-Net: Convolutional networks for biomedical image segmentation. arXiv:1505.04597, May 2015.
10. Zhu W., Liu C., Fan W., Xie X. Deeplung: Deep 3D dual path nets for automated pulmonary nodule detection and classication. arXiv: 1801.09555, Jan 2018.
11. Armato III S.G., McLennan G., and et al. The lung image databas econsortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on ct scans. Medical Physics, 38(2):915-931, 2011.
12. Shao H., Cao L., Liu Y. A detection approach for solitary pulmonary nodules based on CT images. In Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference On. Changchun; 2012:1253–1257.
13. Ye X., Lin X., Dehmeshki J., Slabaugh G., Beddoe G. Shape-based computer-aided detection of lung nodules in thoracic CT images. Biomed Eng IEEE Trans 2009, 56(7):1810–1820.
14. Arimura H., Magome T., Yamashita Y., Yamamoto D. Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms 2009, 2(3):925–952.
15. Liao F., Liang M., Li Z., Hu X., Song S. Evaluate the malignancy of pulmonary nodules using the 3D deep leaky noisy-or network. arXiv:1711.08324, Nov 2017.
16. Messay T., Hardie R., Rogers S. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal, 14(390406), 2010.
17. Pu J., Paik D., Meng X., Roos J., Rubin G. Shape break-and-repair strategy and its application to automated medical image segmentation. IEEE Trans. Visualization Comput. Gr. 2011, 17, 115–124.
18. Suarez-Cuenca J., Tahoces P., Souto M., Lado M., Remy-Jardin M., Remy J., Jose Vidal J. Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images. Comput. Biol. Med. 2009, 39, 921–933.
19. Kubota T., Jerebko A., Dewan M., Salganicoff M., Krishnan A. Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med. Image Anal. 2011, 15, 133–154.
20. Biradar and U. Patil. Computer aided detection (CAD) system for automatic pulmonary nodule detection in lungs in CT scans. The International Journal of Engineering and Science, 2(1):18-21, 2013.
21. Okumura T., Miwa T., Kato J., Yamamoto S., Matsumoto M., Tateno Y., Iinuma T., Matsumoto T. Variable N-Quoit lter applied for automatic detection of lung cancer by X-ray CT. In Computer Assisted Radiology and Surgery (CAR98), pp. 242- 247, Japan, 1998.
22. Walawalkar D. A fully automated framework for lung tumour detection, segmentation and analysis. arXiv:1801.01402, Jan 2018.
23. Khosravan N., Bagci U. Semi-supervised multi-task learning for lung cancer diagnosis. arXiv:1802.06181v1, Feb 2018.
24. Farag A.A., Ali A., Elshazly S., Farag A.A.. Feature fusion for lung nodule classication. International Journal of Computer Assisted Radiology and Surgery, 12(10):1809-1818, 2017.
25. Froz B.R., de C. Filhoa A.O., Silva A.C., de Paiva A.C., Nunes R.A., Gattass M. Lung nodule classication using articial crawlers, directional texture and support vector machine. Expert Systems With Applications, 69:176-188, 2017.
26. Santos A.M., Filho A.O. de C., Silva A.C., de Paiva A.C., Nunes R.A., Gattass M.. Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM. Engineering Applications of Articial Intelligence, 36:27-39, 2014.
27. Mendonca P.R., Bhotika R., Zhao F., Miller J.V. Lung nodule detection via bayesian voxel labeling. In Biennial International Conference on Information Processing in Medical Imaging, pages 134-146, Berlin, Heidelberg, July 2007. Springer.
28. Tan M., Deklerck R., Jansen B., Bister M., Cornelis J. A novel computer-aided lung nodule detection system for CT images. Medical physics, 38(10):5630-5645, 2011.
29. Zhou T., Lu H., Zhang J., Shi H. Pulmonary nodule detection model based on svm and ct image feature-level fusion with rough sets. BioMed Research International, 2016(Article ID 8052436):113, 2016.
30. Suzuki K., Armato S.G., Li F., Sone S., Doi K. Massive training articial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Medical Physics, 30(7):1602-1617, 2003.
31. Kuruvilla J., Gunavathi K. Lung cancer classication using neural networks for CT images. Computer Methods and Programs in Biomedicine, 113:202-209, 2014.
32. Park S.C., Tan J., Wang X., Lederman D., Leader J.K., Kim S.H., and Zheng B. Computer-aided detection of early interstitial lung diseases using low-dose CT images. Physics in Medicine and Biology, 56(1139-1153), 2011.
33. Nithila E.E., Kumar S.S. Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Engineering Science and Technology, an International Journal, 20(3):1192-1202, 2017.
34. John J., Mini M.G. Multilevel thresholding based segmentation and feature extraction for pulmonary nodule detection. Procedia Technology, 24:957-963, 2016.
35. Dandil E., Cakiroglu M., Eksi Z., Ozkan M., Kurt O.K., Canan A. Artificial neural network-based classification system for lung nodules on computed tomography scans. 2014 6th International Conference of Soft Computing and Pattern Recognition, pages 382-386. IEEE, Aug 2014.
36. Zhao B., Cao Z., Wang S. Lung vessel segmentation based on random forests. Electronics Letters, 53(4):220-222, 2017.
37. Dey R., Lu Z., Hong Y. Diagnostic classication of lung nodules using 3D neural networks. arXiv:1803.07192, March 2018.
38. Hamidian S., Sahiner B., Petrick N., Pezeshk A. 3D convolutional neural network for automatic detection of lung nodules in chest CT. Proc SPIE Int Soc Opt Eng, 10134:1013409, Mar 2017.
39. Huang X., Shan J., Vaidya V. Lung nodule detection in CT using 3D convolutional neural networks. In 14th International Symposium on Biomedical Imaging (ISBI 2017), pages 379-383. IEEE, April 2017.
40. Yang H., Yu H., Wang G. Deep learning for the classication of lung nodules. arXiv:1611.06651, Nov 2016.
41. Sakamoto M., Nakano H., Zhao K., Sekiyam T. Lung nodule classication by the combination of fusion classier and cascaded convolutional neural networks. arXiv:1712.02198, Nov 2017.
42. Kang G., Liu K., Hou B., Zhang N. 3D multi-view convolutional neural networks for lung nodule classication. PLoS ONE, 12(11):e0188290, 2017.
43. Liu X., Hou F., Qin H., Hao A. Multi-view multi-scale CNNs for lung nodule type classication from CT images. Pattern Recognition, 77:262-275, 2018.
44. Yuan J., Liu X., Hou F., Qin H., Hao A.. Hybrid-feature-guided lung nodule type classication on CT images. Computers & Graphics, 70:288-299, 2018.
45. Hussein S., Chuquicusma M.M., Kandel P., Bolan C.W., Wallace M.B., Bagci U. Supervised and unsupervised tumor characterization in the deep learning era. arXiv:1801.03230, Jan 2018.
46. Causey J., Zhang J., Ma S., Jiang B., Qualls J., Politte D.G., Prior F., Zhang S., Huang X. Highly accurate model for prediction of lung nodule malignancy with CT scans. arXiv:1802.01756, Feb 2018.
47. Trajanovski S., Mavroeidis D., Swisher C.L., Gebre B.G., Veeling B., Wiemker R., Klinder T., Tahmasebi A., Regis S.M., Wald C., McKee B.J., MacMahon H., Pien H. Towards radiologist-level cancer risk assessment in CT lung screening using deep learning, arXiv:1804.01901, Apr 2018.
48. Mobiny A., Moulik S., Gurcan I., Shah T., Van Nguyen H. Lung cancer screening using adaptive memory-augmented recurrent networks. arXiv:1710.05719, Oct 2017.
49. Lee H., Lee J., Kim H., Cho B., Cho S. Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction. arXiv:1803.00694, March 2018.
50. Winkels M., Cohen T.S. 3D G-CNNs for Pulmonary Nodule Detection. arXiv:1804.04656, Apr 2018.