Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks

Radiology

Volumn 284, Issue 2, 2017, Pages 574-582

  Lakhani, Paras ^a   Sundaram, Baskaran ^a  

^a THOMAS JEFFERSON UNIVERSITY HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DEEP CONVOLUTIONAL NEURAL NETWORK; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE CLASSIFICATION; FEMALE; HUMAN; IMAGE PROCESSING; LUNG TUBERCULOSIS; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; THORAX RADIOGRAPHY; ALGORITHM; CLASSIFICATION; DIAGNOSTIC IMAGING; PROCEDURES; RETROSPECTIVE STUDY;

ALGORITHMS; HUMANS; NEURAL NETWORKS (COMPUTER); RADIOGRAPHY, THORACIC; RETROSPECTIVE STUDIES; ROC CURVE; SENSITIVITY AND SPECIFICITY; TUBERCULOSIS, PULMONARY;

EID: 85025112337     PISSN: 00338419     EISSN: 15271315     Source Type: Journal    
DOI: 10.1148/radiol.2017162326     Document Type: Article

References (38)

1. World Health Organization. Global tuberculosis report 2015. http://apps.who.int/iris/bitstream/10665/191102/1/9789241565059-eng.pdf. Published October 28, 2015. Accessed September 20, 2016.
2. World Health Organization. Systematic screening for active tuberculosis: Principles and recommendations. http://www.who.int/tb/publications/Final-TB-Screening-guidelines.pdf. Published April 2013. Accessed September 20, 2016.
3. Bhalla AS, Goyal A, Guleria R, Gupta AK. Chest tuberculosis: Radiological review and imaging recommendations. Indian J Radiol Imaging 2015;25(3):213-225.
4. Melendez J, Sánchez CI, Philipsen RH, et al. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci Rep 2016;6:25265.
5. Hoog AH, Meme HK, van Deutekom H, et al. High sensitivity of chest radiograph reading by clinical officers in a tuberculosis prevalence survey. Int J Tuberc Lung Dis 2011;15(10):1308-1314.
6. Antani S. Automated Detection of Lung Diseases in Chest X-Rays. A Report to the Board of Scientific Counselors. US National Library of Medicine. https://lhncbc.nlm.nih. gov/system/files/pub9126.pdf. Published April 2015. Accessed September 20, 2016.
7. Jaeger S, Karargyris A, Candemir S, et al. Automatic screening for tuberculosis in chest radiographs: A survey. Quant Imaging Med Surg 2013;3(2):89-99.
8. Pande T, Cohen C, Pai M, Ahmad Khan F. Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: A systematic review. Int J Tuberc Lung Dis 2016;20(9):1226-1230.
9. Maduskar P, Muyoyeta M, Ayles H, Hogeweg L, Peters-Bax L, van Ginneken B. Detection of tuberculosis using digital chest radiography: automated reading vs. interpretation by clinical officers. Int J Tuberc Lung Dis 2013;17(12):1613-1620.
10. Jaeger S, Karargyris A, Candemir S, et al. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging 2014;33(2):233-245.
11. Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis 2015;115(3):211-252.
12. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv preprint. https://arxiv.org/abs/1512.03385. Published December 10, 2015. Accessed September 20, 2016.
13. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998;86(11):2278-2324.
14. Bar Y, Diamant I, Wolf L, Greenspan H. Deep learning with non-medical training used for chest pathology identification. In: Hadjiiski LM, Tourassi GD, eds. Proceedings of SPIE: medical imaging 2015-computer-aided diagnosis. Vol 9414. Bellingham, Wash: International Society for Optics and Photonics, 2015; 94140V.
15. Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computeraided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016;35(5):1285-1298.
16. Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 2015;8:2015-2022.
17. Roth HR, Farag A, Lu L, Turkbey EB, Summers RM. Deep convolutional networks for pancreas segmentation in CT imaging. In: Ourselin S, Styner MA, eds. Proceedings of SPIE: medical imaging 2015-image processing. Vol 9413. Bellingham, Wash: International Society for Optics and Photonics, 2015; 94131G.
18. Zhang W, Li R, Deng H, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 2015;108:214-224.
19. Hwang S, Kim HE, Jeong J, Kim HJ. A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Tourassi GD, Armato SG, eds. Proceedings of SPIE: medical imaging 2016-title. Vol 9785. Bellingham, Wash: International Society for Optics and Photonics, 2016; 97852W.
20. Jaeger S, Candemir S, Antani S, Wáng YX, Lu PX, Thoma G. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg 2014;4(6):475-477.
21. Belarus Tuberculosis Portal. Belarus Public Health Web site. http://obsolete.tuberculosis. by/. Published September 1, 2011. Updated July 17, 2015. Accessed August 20, 2016.
22. Jia Y, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia 2014. New York, NY: ACM, 2014.
23. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates, 2012; 1097-1105.
24. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2015; 1-9.
25. Rasband WS. Image J. U.S. National Institutes of Health, Bethesda, Maryland, USA. http://imagej.nih.gov/ij/. 1997-2016.
26. Hastie T, Tibshirani R, Friedman J. Model assessment and selection. In: The elements of statistical learning. 2nd ed. New York, NY: Springer, 2009; 219-259.
27. Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology 2003;229(1):3-8.
28. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 1988;44(3):837-845.
29. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology 2010;21(1):128-138.
30. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 1997;30 (7):1145-1159.
31. Fawcett T. ROC graphs: Notes and practical considerations for researchers. Mach Learn 2004;31(1):1-38.
32. Agresti A, Coull BA. Approximate is better than "exact" for interval estimation of binomial proportions. Am Stat 1998;52(2):119-126.
33. Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012;16(5):933-951.
34. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436-444.
35. Wu R, Yan S, Shan Y, Dang Q, Sun G. Deep image: Scaling up image recognition. arXiv preprint. https://arxiv.org/abs/1501.02876. Published January 13, 2015. Updated July 6, 2015. Accessed September 21, 2016.
36. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15(1):1929-1958.
37. Dietterich TG. Ensemble methods in machine learning. Lect Notes Comput Sci 2000;1857:1-15.
38. Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H. Understanding neural networks through deep visualization. arXiv preprint. https://arxiv.org/abs/1506.06579. Published June 22, 2015. Accessed September 21, 2016.