Computer-aided classification of lung nodules on computed tomography images via deep learning technique

OncoTargets and Therapy

Volumn 8, Issue , 2015, Pages 2015-2022

  Hua, Kai Lung ^a   Hsu, Che Hao ^a   Hidayati, Shintami Chusnul ^a   Cheng, Wen Huang ^b   Chen, Yu Jen ^b  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b MACKAY MEMORIAL HOSPITAL   (Taiwan)

Author keywords

Convolutional neural network; Deep belief network; Deep learning; Nodule classification

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; CONVOLUTIONAL NEURAL NETWORK; DEEP BELIEF NETWORK; DEEP LEARNING; DISEASE CLASSIFICATION; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; LUNG NODULE; MACHINE LEARNING; PATTERN RECOGNITION; SENSITIVITY AND SPECIFICITY;

EID: 84939781083     PISSN: None     EISSN: 11786930     Source Type: Journal    
DOI: 10.2147/OTT.S80733     Document Type: Article

References (21)

1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11-30.
2. Gomez Leon N, Escalona S, Bandres B, et al. F-fluorodeoxyglucose positron emission tomography/computed tomography accuracy in the staging of non-small cell lung cancer: review and cost-effectiveness. Radiol Res Pract. 2014;2014:135934
3. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143 (5 Suppl):e93S-e120S.
4. Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph. 2007; 31(4-5):198-211.
5. van Ginneken B, Schaefer-Prokop CM, Prokop M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology. 2011;261(3):719-732.
6. Cheng J-Z, Chou Y-H, Huang C-S, et al. Computer-aided US diagnosis of breast lesions by using cell-based contour grouping. Radiology. 2010;255(3):746-754.
7. Farag A, Ali A, Graham J, Elshazly S, Falk R. Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest. Paper presented at the Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium, Chicago, IL, USA, March 30 to April 2, 2011.
8. Farag A, Graham J, Elshazly S. Statistical modeling of the lung nodules in low dose computed tomography scans of the chest. Paper presented at Image Processing (ICIP), 17th IEEE International Conference, Hong Kong, People’s Republic of China, September 26-29, 2010.
9. Lin P-L, Huang P-W, Lee C-H, Wu M-T. Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model. Pattern Recognit. 2013;46(12): 3279-3287.
10. Singh S, Maxwell J, Baker JA, Nicholas JL, Lo JY. Computer-aided classification of breast masses: Performance and interobserver variability of expert radiologists versus residents. Radiology. 2011;258(1): 73-80.
11. Way TW, Sahiner B, Chan H-P, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys. 2009;36:3086.
12. Raykar VC, Yu S, Zhao LH, et al. Supervised learning from multiple experts: whom to trust when everyone lies a bit. Paper presented at the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada, June 14-18, 2009.
13. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE Inst Electr Electron Eng. 1998;86(11): 2278-2324.
14. Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527-1554.
15. Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst. 2007;19:153.
16. Suk H-I, Shen D. Deep learning-based feature representation for AD/MCI classification. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N, editors. Medical Image Computing and Computer-Assisted Intervention MICCAI 2013. Berlin, Germany: Springer; 2013.
17. Lee H, Grosse R, Ranganath R, Ng AY. Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun ACM. 2011;54(10):95-103.
18. Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Paper presented at Advances in Neural Information Processing Systems 2012. Available from: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutionalneural-networks. Accessed May 20, 2015
19. Armato SG, McLennan G, Bidaut L, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011;38:915
20. Armato SG III, McLennan G, McNitt-Gray MF, et al. Lung image database consortium: developing a resource for the medical imaging research community. Radiology. 2004;232(3):739-748.
21. Farag A, Elhabian S, Graham J, Farag A, Falk R. Toward precise pulmonary nodule descriptors for nodule type classification. Medical Image Computing and Computer-Assisted Intervention MICCAI 2010: Berlin, Germany: Springer; 2010