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Classification of lung nodules in CT images using conditional generative adversarial – convolutional neural network | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 1047-1058 اصل مقاله (1.25 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5551 | ||
| نویسندگان | ||
| Nur Nabila Mohd Isham؛ Siti Salasiah Mokri* ؛ Ashrani Aizuddin Abd Rahni؛ Nurul Fatihah Ali | ||
| Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan, Malaysia | ||
| تاریخ دریافت: 19 خرداد 1400، تاریخ پذیرش: 16 شهریور 1400 | ||
| چکیده | ||
| Based on Global Cancer 2015 statistics, the lung cancer of all types constitutes 27% of overall cancers while 19.5% of cancer deaths are due to lung cancer. In lieu of this, an effective lung cancer screening test using Computed Tomography (CT) scan is crucial to detect cancer at the early stage. The interpretation of the CT images requires an advanced CAD system of high accuracy for instance, in classifying the lung nodules. Recently, Deep Learning method that is Convolution Neural Network (CNN) shows an outstanding success in lung nodules classification. However, the training of CNN requires a great number of images. Such a requirement is an issue in the case of medical images. Generative adversarial network (GAN) has been introduced to generate new image datasets for CNN training. Thus, the main objective of this study is to compare the performance of CNN architectures with and without the implementation of GAN for lung nodules classification in CT images. Here, the study used Conditional GAN (cGAN) to generate benign nodules images. The classification accuracy of the combined cGAN-CNN architecture was compared among CNN pretraining networks namely GoogleNet, ShuffleNet, DenseNet, and MobileNet based on classification accuracy, specificity, sensitivity, and AUC-ROC values. The experiment was tested on LIDC-IDRI database. The results showed cGAN-CNN architecture improves the overall classification accuracy as compared to CNN alone with the cGAN-ShuffleNet architecture performed the best, achieving 98.38% accuracy, 98.13% specificity, 100% sensitivity and AUC-ROC at 99.90%. Overall, the classification performance of CNN can be improved by integrating GAN architecture to mitigate the constraint of having a large medical image dataset, in this case, CT lung nodules images. | ||
| کلیدواژهها | ||
| Computed Tomography؛ Convolution neural network؛ Generative adversarial network؛ Lung nodules؛ Classification | ||
| مراجع | ||
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[1] S. Armato III, G. Mclennan, L. Bidaut, M. McNitt-Gray, C. Meyer, A. Reeves, B. Zhao, B., et al, The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans, Medical Physics, 38(2011) 915–93, doi: 10.1118/1.3528204. [2] S.M. Ashhar, S.S. Mokri, A.A. Abd Rahni, A.B. Huddin, N. Zulkarnain, N.A. Azmi, and T. Mahaletchumy, Comparison of deep learning convolutional neural network (CNN) architectures for CT lung cancer classification, International Journal of Advanced Technology and Engineering Exploration, 8 (74)(2021)126, doi: 10.19101/IJATEE.2020.S1762126. [3] M. Bittoni, J.C. Yang, J.Y. Shih, N. Peled, E.F. Smit, D.R. Camidge, and P.K. Paik, Real-world insights into patients with advanced NSCLC and MET alterations. Lung Cancer, 159 (2021) 96-106, doi: 10.1016/j.lungcan.2021.06.015. [4] S.A. El-Regaily, M.A. Salem, M.H. Abdel Aziz, and M.I. Roushdy, Survey of Computer Aided Detection Systems for Lung Cancer in Computed Tomography, Current Medical Imaging Reviews, 14 (1)(2017) 3–18, doi: 10.2174/1573405613666170602123329. [5] G. Huang, and K.Q. Weinberger, Densely Connected Convolutional Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2018) 2261-2269, doi: 10.1109/CVPR.2017.243. [6] C.S. Kan, and K. Chan, A Review of Lung Cancer Research in Malaysia, The Medical Journal of Malaysia, (71) (2016)70–78, PMID: 27801389. [7] G. Kang, K. Liu, B. Hou, and N. Zhang N, 3D multi-view convolutional neural networks for lung nodule classification, PLoS ONE, 12(11)(2017) e0188290, doi: 10.1371/journal.pone.0188290. [8] K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, and D.I. Fotiadis, Machine learning applications in cancer prognosis and prediction, Computational and Structural Biotechnology Journal, 13(2015) 8–17, doi: 10.1016/j.csbj.2014.11.005. [9] Y.A.N. Kuang, T. Lan, and X. Peng, Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification, IEEE Access, 8 (2020) 77725–77734, doi: 10.1109/ACCESS.2020.2987961. [10] J.H.B. Masud, Lung diseases and smoking: A systematic analysis of big data in the era of artificial intelligence, Tobacco Induced Diseases, 19(1)( 2021) , doi:10.18332/tid/141067. [11] M. Mirza, and S. Osindero, Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784, 2014. [12] Y. Onishi, A. Teramoto, M. Tsujimoto, T. Tsukamoto, K. Saito, H. Toyama, K. Imaizumi, and et al, Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks, Biomed Res Int., 2019 (6051939) 2019, doi: 10.1155/2019/6051939. [13] Y. Onishi, A. Teramoto, M. Tsujimoto, T. Tsukamoto, K. Saito, and H. Toyama, Investigation of pulmonary nodule classification using multi - scale residual network enhanced with 3DGAN - synthesized volumes, Radiol Phys Technol, 13(2)(2020) 160-169, doi: 10.1007/s12194-020-00564-5. [14] Y. Onishi, A. Teramoto, M. Tsujimoto, T. Tsukamoto, K. Saito, and H. Toyama, Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks, Int J Comput Assist Radiol Surg, 15(1)(2020) 173-178, doi: 10.1007/s11548-019-02092-z. [15] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.C. Chen, MobileNetV2: Inverted Residuals and Linear Bottlenecks, IEEE Conference on Computer Vision and Pattern Recognition, (2018) 4510-4520, doi: 10.1109/CVPR.2017.243. [16] Q.Z. Song, L. Zhao, X.K. Luo, and X.C. Dou, Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images, Journal of Healthcare Engineering, 2017 (Article ID 8314740) (2017), doi: 10.1155/2017/8314740. [17] C. Szegedy, S. Reed, P. Sermanet, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015) 1-9, doi: 10.1109/CVPR.2015.7298594. [18] A. Teramoto, H. Fujita, O. Yamamuro, and T. Tamaki, Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique, Medical Physics, 43 (6)(2016) 2821–2827, doi: 10.1118/1.4948498. [19] G.S. Tran, T.P. Nghiem, V.T. Nguyen, C.M. Luong, J.C. Burie, and Y. Levin-Schwartz, Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss, Journal of Healthcare Engineering, 2019 (2019), doi: 10.1155/2019/5156416. [20] X. Yi, E. Walia, and P. Babyn, Generative adversarial network in medical imaging: A review, Medical image analysis, 58 (2018) 101552, doi: 10.1016/j.media.2019.101552. [21] X. Zhang, X. Zhou, M. Lin, and J. Sun, Shufflenet: An extremely efficient convolutional neural network for mobile devices, In Proceedings of the IEEE conference on computer vision and pattern recognition, (2018) 6848-6856, doi: 10.1109/CVPR.2018.00716.[22] D. Zhao, D. Zhu, J. Lu, and Y. Luo, Synthetic medical images using F&BGAN for improved lung nodules classification by multi-scale VGG16. Symmetry, 10 (519) (2018), doi: 10.3390/sym10100519. [23] M. Zhang, N. Zhuo, Z. Guo, X. Zhang, W. Liang, S. Zhao, and J. He, Establishment of a mathematic model for predicting malignancy in solitary pulmonary nodules, Journal of thoracic disease, vol. 7(10) (2015) 1833-1841, doi: 10.3978/j.issn.2072-1439.2015.10.56. | ||
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