Lung cancer classification with Convolutional Neural Network Architectures
DOI:
https://doi.org/10.48161/qaj.v1n1a33Keywords:
Deep learning; Transfer learning; Lung cancer; pre-trained network.Abstract
One of the most common malignant tumors in the world today is lung cancer, and it is the primary cause of death from cancer. With the continuous advancement of urbanization and industrialization, the problem of air pollution has become more and more serious. The best treatment period for lung cancer is the early stage. However, the early stage of lung cancer often does not have any clinical symptoms and is difficult to be found. In this paper, lung nodule classification has been performed; the data have used of CT image is SPIE AAPM-Lung. In recent years, deep learning (DL) was a popular approach to the classification process. One of the DL approaches that have used is Transfer Learning (TL) to eliminate training costs from scratch and to train for deep learning with small training data. Nowadays, researchers have been trying various deep learning techniques to improve the efficiency of CAD (computer-aided system) with computed tomography in lung cancer screening. In this work, we implemented pre-trained CNN include: AlexNet, ResNet18, Googlenet, and ResNet50 models. These networks are used for training the network and CT image classification. CNN and TL are used to achieve high performance resulting and specify lung cancer detection on CT images. The evaluation of models is calculated by some matrices such as confusion matrix, precision, recall, specificity, and f1-score.
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O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015, doi: 10.1007/s11263-015-0816-y.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, 2010, doi: 10.1109/TKDE.2009.191.
G. E. H. Alex Krizhevsky, Ilya Sutskever, “Handbook of approximation algorithms and metaheuristics,” Handb. Approx. Algorithms Metaheuristics, pp. 1–1432, 2007, doi: 10.1201/9781420010749.
V. Sangeetha and K. J. R. Prasad, “Syntheses of novel derivatives of 2-acetylfuro[2,3-a]carbazoles, benzo[1,2-b]-1,4-thiazepino[2,3-a]carbazoles and 1-acetyloxycarbazole-2- carbaldehydes,” Indian J. Chem. - Sect. B Org. Med. Chem., vol. 45, no. 8, pp. 1951–1954, 2006, doi: 10.1002/chin.200650130.
C. Szegedy et al., “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015, doi: 10.1109/CVPR.2015.7298594.
E. Cengil and A. Çinar, “A Deep Learning Based Approach to Lung Cancer Identification,” 2018 Int. Conf. Artif. Intell. Data Process. IDAP 2018, 2019, doi: 10.1109/IDAP.2018.8620723.
Z. Shi et al., “A deep CNN based transfer learning method for false positive reduction,” Multimed. Tools Appl., vol. 78, no. 1, pp. 1017–1033, 2019, doi: 10.1007/s11042-018-6082-6.
R. Anirudh, J. J. Thiagarajan, T. Bremer, and H. Kim, “Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data,” Med. Imaging 2016 Comput. Diagnosis, vol. 9785, no. November 2017, p. 978532, 2016, doi: 10.1117/12.2214876.
Q. Z. Song, L. Zhao, X. K. Luo, and X. C. Dou, “Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images,” J. Healthc. Eng., vol. 2017, 2017, doi: 10.1155/2017/8314740.
R. M. Devarapalli, H. K. Kalluri, and V. Dondeti, “Lung cancer detection of ct lung images,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 413–416, 2019.
V. Makde, J. Bhavsar, S. Jain, and P. Sharma, “Deep neural network based classification of tumourous and non-tumorous medical images,” Smart Innov. Syst. Technol., vol. 84, no. Ictis 2017, pp. 199–206, 2018, doi: 10.1007/978-3-319-63645-0_22.
L. P. Armato III, Samuel G.; Hadjiiski, Lubomir; Tourassi, Georgia D.; Drukker, Karen; Giger, Maryellen L.; Li, Feng; Redmond, George; Farahani, Keyvan; Kirby, Justin S.; Clarke, “SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset,” 2015, [Online]. Available: https://doi.org/10.7937/K9/TCIA.2015.UZLSU3FL.
H. C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–1298, 2016, doi: 10.1109/TMI.2016.2528162.
X. Cui, W. Zhang, Z. Tüske, and M. Picheny, “Evolutionary stochastic gradient descent for optimization of deep neural networks,” Adv. Neural Inf. Process. Syst., vol. 2018-Decem, no. Nips, pp. 6048–6058, 2018.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
L. Alzubaidi, O. Al-Shamma, M. A. Fadhel, L. Farhan, J. Zhang, and Y. Duan, “Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model,” Electron., vol. 9, no. 3, 2020, doi: 10.3390/electronics9030445.
L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” 2017, [Online]. Available: http://arxiv.org/abs/1712.04621.
H. M and S. M.N, “A Review on Evaluation Metrics for Data Classification Evaluations,” Int. J. Data Min. Knowl. Manag. Process, vol. 5, no. 2, pp. 01–11, 2015, doi: 10.5121/ijdkp.2015.5201.
H. Jiang, H. Ma, W. Qian, M. Gao, and Y. Li, “An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 4, pp. 1227–1237, 2018, doi: 10.1109/JBHI.2017.2725903.
R. V. M. Da Nóbrega, S. A. Peixoto, S. P. P. Da Silva, and P. P. R. Filho, “Lung Nodule Classification via Deep Transfer Learning in CT Lung Images,” Proc. - IEEE Symp. Comput. Med. Syst., vol. 2018-June, pp. 244–249, 2018, doi: 10.1109/CBMS.2018.00050.
T. K. Sajja, R. M. Devarapalli, and H. K. Kalluri, “Lung cancer detection based on CT scan images by using deep transfer learning,” Trait. du Signal, vol. 36, no. 4, pp. 339–344, 2019, doi: 10.18280/ts.360406.
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