Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

General Research Article

Lung Nodule Detection using Convolutional Neural Networks with Transfer Learning on CT Images

Author(s): Jun Gao*, Qian Jiang, Bo Zhou and Daozheng Chen

Volume 24, Issue 6, 2021

Published on: 13 July, 2020

Page: [814 - 824] Pages: 11

DOI: 10.2174/1386207323666200714002459

Price: $65

Abstract

Aim and Objective: Lung nodule detection is critical in improving the five-year survival rate and reducing mortality for patients with lung cancer. Numerous methods based on Convolutional Neural Networks (CNNs) have been proposed for lung nodule detection in Computed Tomography (CT) images. With the collaborative development of computer hardware technology, the detection accuracy and efficiency can still be improved.

Materials and Methods: In this study, an automatic lung nodule detection method using CNNs with transfer learning is presented. We first compared three of the state-of-the-art convolutional neural network (CNN) models, namely, VGG16, VGG19 and ResNet50, to determine the most suitable model for lung nodule detection. We then utilized two different training strategies, namely, freezing layers and fine-tuning, to illustrate the effectiveness of transfer learning. Furthermore, the hyper-parameters of the CNN model such as optimizer, batch size and epoch were optimized.

Results: Evaluated on the Lung Nodule Analysis 2016 (LUNA16) challenge, promising results with an accuracy of 96.86%, a precision of 91.10%, a sensitivity of 90.78%, a specificity of 98.13%, and an AUC of 99.37% were achieved.

Conclusion: Compared with other works, state-of-the-art specificity is obtained, which demonstrates that the proposed method is effective and applicable to lung nodule detection.

Keywords: Lung nodule detection, CNNs, CT, transfer learning, medical image analysis, deep learning.

[1]
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2015. CA Cancer J. Clin., 2015, 65(1), 5-29.
[http://dx.doi.org/10.3322/caac.21254] [PMID: 25559415]
[2]
Dhara, A.K.; Mukhopadhyay, S.; Khandelwal, N. Computer-aided detection and analysis of pulmonary nodule from ct images: a survey. IETE Tech. Rev., 2012, 29(4), 265-275.
[http://dx.doi.org/10.4103/0256-4602.101306]
[3]
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.
[http://dx.doi.org/10.1016/j.compmedimag.2007.02.002] [PMID: 17349778]
[4]
Valente, I.R.S.; Cortez, P.C.; Neto, E.C.; Soares, J.M.; de Albuquerque, V.H.C.; Tavares, J.M.R.S. Automatic 3D pulmonary nodule detection in CT images: A survey. Comput. Methods Programs Biomed., 2016, 124, 91-107.
[http://dx.doi.org/10.1016/j.cmpb.2015.10.006] [PMID: 26652979]
[5]
Saba, L.; Caddeo, G.; Mallarini, G. Computer-aided detection of pulmonary nodules in computed tomography: analysis and review of the literature. J. Comput. Assist. Tomogr., 2007, 31(4), 611-619.
[http://dx.doi.org/10.1097/rct.0b013e31802e29bf] [PMID: 17882043]
[6]
Arevalo, J.; Gonzàlez, F.A.; Ramos-Pollàn, R.; Oliveira, J.L.; Guevara Lopez, M.A. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed., 2016, 127, 248-257.
[http://dx.doi.org/10.1016/j.cmpb.2015.12.014] [PMID: 26826901]
[7]
Gao, X.W.; Hui, R.; Tian, Z. Classification of CT brain images based on deep learning networks. Comput. Methods Programs Biomed., 2017, 138, 49-56.
[http://dx.doi.org/10.1016/j.cmpb.2016.10.007] [PMID: 27886714]
[8]
Silva, G.L.; Neto, P.S.; Silva, C.; Paiva, A.C.; Gattass, M. Lung nodules diagnosis based on evolutionary convolutional neural network. Multimedia Tools Appl., 2017, 76(18), 19039-19055.
[http://dx.doi.org/10.1007/s11042-017-4480-9]
[9]
Demir, O.; Camurcu, A.Y. Computer-aided detection of lung nodules using outer surface features. Biomed. Mater. Eng., 2015, 26(1)(Suppl. 1), S1213-S1222.
[http://dx.doi.org/10.3233/BME-151418] [PMID: 26405880]
[10]
Nishio, M.; Nishizawa, M.; Sugiyama, O.; Kojima, R.; Yakami, M.; Kuroda, T.; Togashi, K. Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. PLoS One, 2018, 13(4), e0195875.
[http://dx.doi.org/10.1371/journal.pone.0195875] [PMID: 29672639]
[11]
Nishio, M.; Sugiyama, O.; Yakami, M.; Ueno, S.; Kubo, T.; Kuroda, T.; Togashi, K. Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PLoS One, 2018, 13(7), e0200721.
[http://dx.doi.org/10.1371/journal.pone.0200721] [PMID: 30052644]
[12]
Tan, J.; Huo, Y.; Liang, Z.; Li, L. Expert knowledge-infused deep learning for automatic lung nodule detection. J. XRay Sci. Technol., 2019, 27(1), 17-35.
[http://dx.doi.org/10.3233/XST-180426] [PMID: 30452432]
[13]
Armato, S.G., III; McLennan, G.; Bidaut, L.; McNitt-Gray, M.F.; Meyer, C.R.; Reeves, A.P.; Zhao, B.; Aberle, D.R.; Henschke, C.I.; Hoffman, E.A.; Kazerooni, E.A.; MacMahon, H.; Van Beeke, E.J.R.; Yankelevitz, D.; Biancardi, A.M.; Bland, P.H.; Brown, M.S.; Engelmann, R.M.; Laderach, G.E.; Max, D.; Pais, R.C.; Qing, D.P.Y.; Roberts, R.Y.; Smith, A.R.; Starkey, A.; Batrah, P.; Caligiuri, P.; Farooqi, A.; Gladish, G.W.; Jude, C.M.; Munden, R.F.; Petkovska, I.; Quint, L.E.; Schwartz, L.H.; Sundaram, B.; Dodd, L.E.; Fenimore, C.; Gur, D.; Petrick, N.; Freymann, J.; Kirby, J.; Hughes, B.; Casteele, A.V.; Gupte, S.; Sallamm, M.; Heath, M.D.; Kuhn, M.H.; Dharaiya, E.; Burns, R.; Fryd, D.S.; Salganicoff, M.; Anand, V.; Shreter, U.; Vastagh, S.; Croft, B.Y. 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(2), 915-931.
[http://dx.doi.org/10.1118/1.3528204] [PMID: 21452728]
[14]
Gu, Y.; Lu, X.; Zhang, B.; Zhao, Y.; Yu, D.; Gao, L.; Cui, G.; Wu, L.; Zhou, T. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography. PLoS One, 2019, 14(1), e0210551.
[http://dx.doi.org/10.1371/journal.pone.0210551] [PMID: 30629724]
[15]
Jia, T.; Zhang, H.; Meng, H. A novel lung nodules detection scheme based on vessel segmentation on CT images. Biomed. Mater. Eng., 2014, 24(6), 3179-3186.
[http://dx.doi.org/10.3233/BME-141139] [PMID: 25227026]
[16]
Jose, D.; Chithara, A.N.; Nirmal Kumar, P.; Kareemulla, H. Automatic detection of lung cancer nodules in computerized tomography images. Natl. Acad. Sci. Lett., 2017, 40(3), 161-166.
[http://dx.doi.org/10.1007/s40009-017-0549-2]
[17]
da Silva, G.L.F.; Valente, T.L.A.; Silva, A.C.; de Paiva, A.C.; Gattass, M. Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput. Methods Programs Biomed., 2018, 162, 109-118.
[http://dx.doi.org/10.1016/j.cmpb.2018.05.006] [PMID: 29903476]
[18]
Li, Y.; Zhang, L.; Chen, H.; Yang, N. Lung nodule detection with deep learning in 3D thoracic MR images. IEEE Access, 2019, 37822-37832.
[http://dx.doi.org/10.1109/ACCESS.2019.2905574]
[19]
Dou, Q.; Chen, H.; Yu, L.; Qin, J.; Heng, P.A. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng., 2017, 64(7), 1558-1567.
[http://dx.doi.org/10.1109/TBME.2016.2613502] [PMID: 28113302]
[20]
Cao, H.; Liu, H.; Song, E.; Ma, G.; Xu, X.; Jin, R.; Liu, T.; Hung, C. Multi-branch ensemble learning architecture based on 3D CNN for false positive reduction in lung nodule detection. IEEE Access, 2019, 67380-67391.
[http://dx.doi.org/10.1109/ACCESS.2019.2906116]
[21]
Shi, Z.; Hao, H.; Zhao, M-h.; Feng, Y.; He, L.; Wang, Y.; Suzuki, K. A deep CNN based transfer learning method for false positive reduction. Multimedia Tools Appl., 2018, 78(1), 1017-1033.
[http://dx.doi.org/10.1007/s11042-018-6082-6]
[22]
Xie, Y.; Xia, Y.; Zhang, J.; Song, Y.; Feng, D.; Fulham, M.; Cai, W. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans. Med. Imaging, 2019, 38(4), 991-1004.
[http://dx.doi.org/10.1109/TMI.2018.2876510] [PMID: 30334786]
[23]
Setio, A.A.A.; Traverso, A.; de Bel, T. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med. Image Anal., 2017, 42, 1-13.
[http://dx.doi.org/10.1016/j.media.2017.06.015] [PMID: 28732268]
[24]
Messay, T.; Hardie, R.C.; Tuinstra, T.R. Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Med. Image Anal., 2015, 22(1), 48-62.
[http://dx.doi.org/10.1016/j.media.2015.02.002] [PMID: 25791434]
[25]
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, 2015, 28(1), 99-115.
[http://dx.doi.org/10.1007/s10278-014-9718-8] [PMID: 25117512]
[26]
Zhang, W.; Wang, X.; Zhang, P.; Chen, J. Global optimal hybrid geometric active contour for automated lung segmentation on CT images. Comput. Biol. Med., 2017, 91, 168-180.
[http://dx.doi.org/10.1016/j.compbiomed.2017.10.005] [PMID: 29080491]
[27]
Shen, S.; Bui, A.A.T.; Cong, J.; Hsu, W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput. Biol. Med., 2015, 57, 139-149.
[http://dx.doi.org/10.1016/j.compbiomed.2014.12.008] [PMID: 25557199]
[28]
Soliman, A.; Khalifa, F.; Elnakib, A. Accurate lungs segmentation on CT chest images by adaptive appearance-guided shape modeling. IEEE Trans. Med. Imaging, 2017, 36(1), 263-276.
[http://dx.doi.org/10.1109/TMI.2016.2606370] [PMID: 27705854]
[29]
Hosseini-Asl, E.; Zurada, J.M.; Gimelfarb, G.; El-Baz, A. 3-D lung segmentation by incremental constrained nonnegative matrix factorization. IEEE Trans. Biomed. Eng., 2016, 63(5), 952-963.
[http://dx.doi.org/10.1109/TBME.2015.2482387] [PMID: 26415200]
[30]
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. 2016IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016New York2016, pp. 770-778.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[31]
Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science, 2014. arXiv:1409.1556.
[32]
Bradley, A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit., 1997, 30(7), 1145-1159.
[http://dx.doi.org/10.1016/S0031-3203(96)00142-2]
[33]
S.K, L.; Mohanty, S.N. K, S. Optimal deep learning model for classification of lung cancer on CT images. Fut. Gen. Comput. Syst., 2019, 374-382.
[34]
Zuo, W.; Zhou, F.; Li, Z.; Wang, L. Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access, 2019, 32510-32521.
[http://dx.doi.org/10.1109/ACCESS.2019.2903587]
[35]
Tran, G.S.; Nghiem, T.P.; Nguyen, V.T.; Luong, C.M.; Burie, J-C. Improving accuracy of lung nodule classification using deep learning with focal loss. J. Healthc. Eng., 2019, 2019, 5156416.
[http://dx.doi.org/10.1155/2019/5156416] [PMID: 30863524]
[36]
Shen, S.; Han, S.X.; Aberle, D.R.; Bui, A.A.T.; Hsu, W. An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Syst. Appl., 2019, 128, 84-95.
[http://dx.doi.org/10.1016/j.eswa.2019.01.048] [PMID: 31296975]
[37]
Ali, I.; Hart, G.R.; Gunabushanam, G.; Liang, Y.; Muhammad, W.; Nartowt, B.; Kane, M.; Ma, X.; Deng, J. Lung nodule detection via deep reinforcement learning. Front. Oncol., 2018, 8, 108.
[http://dx.doi.org/10.3389/fonc.2018.00108] [PMID: 29713615]
[38]
Naqi, S.M.; Muhammad, S.; Mussarat, Y.; Steven Lawrence, F. Lung nodule detection using polygon approximation and hybrid features from CT images. Current Medical Imaging, 2018, 14(1), 108-117.
[http://dx.doi.org/10.2174/1573405613666170306114320]
[39]
Liu, Y.; Hao, P.; Zhang, P.; Xu, X.; Wu, J.; Chen, W. Dense convolutional binary-tree networks for lung nodule classification. IEEE Access, 2018, 49080-49088.
[http://dx.doi.org/10.1109/ACCESS.2018.2865544]
[40]
Farahani, F.V.; Ahmadi, A.; Zarandi, M.H.F. Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Math. Comput. Simul., 2018, 48-68.
[http://dx.doi.org/10.1016/j.matcom.2018.02.001]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy