LUNG CANCER DETECTION BY HYBRID LEARNING METHOD APPLYING SMOTE TECHNIQUE

Abstract

Lung cancer is a very deadly disease. However, early diagnosis and detection is an essential factor in overcoming this deadly disease. Tumors formed in this disease's initial stage are divided into benign and malignant. These can be visualized using a computed tomography (CT) scan. Thanks to machine learning and deep learning, cancer stages can be detected using these images. In our study, the best and most promising results in the literature were obtained by using a hybrid learning architecture. The data mining techniques we use in obtaining these results also play a significant role. The best accuracy result we obtained belongs to the CNN+GBC hybrid algorithm, which we recommend with 99.71%.

Keywords

• Lung cancer detection
• deep learning
• hybrit learning
• classification

Kaynakça

1. [1] Malhotra, J., et al., Risk factors for lung cancer worldwide. European Respiratory Journal, 2016. 48(3): p. 889-902.
2. [2] Society, A.C. Key statistics for lung cancer. 2021 2 August 2021]; Available from: https://www.cancer.org/cancer/lung-cancer/about/key-statistics.html#written_by.
3. [3] Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 2021. 71(3): p. 209-249.
4. [4] Saeed, S., et al., Optimized Breast Cancer Premature Detection Method With Computational Segmentation: A Systematic Review Mapping, in Approaches and Applications of Deep Learning in Virtual Medical Care, N. Zaman, L. Gaur, and M. Humayun, Editors. 2022, IGI Global: Hershey, PA, USA. p. 24-51.
5. [5] Nall, R. What to Know about Lung Cancer. 2018 2 April 2022]; Available from: https://www.medicalnewstoday.com/articles/323701.
6. [6] Lung Cancer Risk Factors. [cited 2022; Available from: https://www.cancer.org/cancer/lung-cancer/causes-risks-prevention/risk-factors.html.
7. [7] Kay, F.U., et al., Revisions to the Tumor, Node, Metastasis staging of lung cancer: Rationale, radiologic findings and clinical implications. World journal of radiology, 2017. 9(6): p. 269.
8. [8] Svoboda, E., Artificial intelligence is improving the detection of lung cancer. Nature, 2020. 587(7834): p. S20-S20.

9. [9] Kent, J., Google develops deep learning tool to enhance lung cancer detection. Health IT Analytics., 2019.
10. [10] Jhohnson, K. Google’s lung cancer detection AI outperforms 6 human radiologists. 2019 2 August 2021]; Available from: https://venturebeat.com/2019/05/20/googles-lung-cancer-detection-ai-outperforms-6-human-radiologists/.
11. [11] Lyu, L. Lung Cancer Diagnosis Based on Convolutional Neural Networks Ensemble Model. in 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). 2021. IEEE.
12. [12] Ardila, D., et al., End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 2019. 25(6): p. 954-961.
13. [13] Alakwaa, W., M. Nassef, and A. Badr, Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). International Journal of Advanced Computer Science and Applications, 2017. 8(8).
14. [14] Welch, H.G., L.M. Schwartz, and S. Woloshin, Are Increasing 5-Year Survival Rates Evidence of Success Against Cancer? JAMA, 2000. 283(22): p. 2975-2978.
15. [15] Krizhevsky, A., I. Sutskever, and G.E. Hinton, ImageNet classification with deep convolutional neural networks. InAdvances in Neural Information Processing Systems 25. Go to reference in article, 2012.
16. [16] Simonyan, K. and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
17. [17] Jiao, Z., et al., A deep feature based framework for breast masses classification. Neurocomputing, 2016. 197: p. 221-231.
18. [18] Huang, G., et al., Densely connected convolutional networks. CVPR. IEEE Computer Society, 2017: p. 2261-2269.
19. [19] Gupta, P. and A.P. Shukla. Improving Accuracy of Lung Nodule Classification Using AlexNet Model. in 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). 2021.
20. [20] Shimazaki, A., et al., Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method. Scientific Reports, 2022. 12(1): p. 727.
21. [21] Alyasriy, H. and A. Muayed, The IQ-OTHNCCD lung cancer dataset. Mendeley Data, 2021. 1: p. 2020.
22. [22] Chawla, N.V., et al., SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 2002. 16: p. 321-357.
23. [23] Plamondon, R. and S.N. Srihari, Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(1): p. 63-84.
24. [24] Zhuang, B., et al. Towards effective low-bitwidth convolutional neural networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
25. [25] Feng, Y., et al. Gvcnn: Group-view convolutional neural networks for 3d shape recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
26. [26] Chen, Y.H., et al., Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks. IEEE Journal of Solid-State Circuits, 2017. 52(1): p. 127-138.
27. [27] Chen, L.C., et al., DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. 40(4): p. 834-848.
28. [28] Acharya, U.R., et al., Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 2018. 100: p. 270-278.
29. [29] Safavian, S.R. and D. Landgrebe, A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 1991. 21(3): p. 660-674.
30. [30] Wager, S. and S. Athey, Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association, 2018. 113(523): p. 1228-1242.
31. [31] Song, G., et al., K Nearest Neighbour Joins for Big Data on MapReduce: A Theoretical and Experimental Analysis. IEEE Transactions on Knowledge and Data Engineering, 2016. 28(9): p. 2376-2392.
32. [32] Zhang, X., et al., KRNN: k Rare-class Nearest Neighbour classification. Pattern Recognition, 2017. 62: p. 33-44.
33. [33] Amor, N.B., S. Benferhat, and Z. Elouedi. Naive bayes vs decision trees in intrusion detection systems. in Proceedings of the 2004 ACM symposium on Applied computing. 2004.
34. [34] Ravi, V., D. Pradeepkumar, and K. Deb, Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms. Swarm and Evolutionary Computation, 2017. 36: p. 136-149.
35. [35] Tama, B.A. and K.-H. Rhee, An in-depth experimental study of anomaly detection using gradient boosted machine. Neural Computing and Applications, 2019. 31(4): p. 955-965.
36. [36] Zhao, Y., et al., Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis. Biosystems Engineering, 2016. 148: p. 127-137.
37. [37] Humayun, M., et al., A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma. Healthcare, 2022. 10(6): p. 1058.
38. [38] Naseer, I., et al., Performance Analysis of State-of-the-Art CNN Architectures for LUNA16. Sensors, 2022. 22(12): p. 4426.
39. [39] Mohite, A., Application of Transfer Learning Technique for Detection and Classification of Lung Cancer using CT Images.