jzs-10749

A Deep Learning Technique For Lung Nodule Classification Based on False Positive Reduction

Hunar Abubakir Ahmed1 & Sozan Abdulla Mahmood2

1 College of Science - Raparin University, Sulaimani-Iraq 
2 College of Science – Sulaimani university , Sulaimani-Iraq

Original: 14 December 2018  
Revised: 14 January 2019
Accepted: 17 February 2019
Published online: 20 June 2019  



Abstract

Cancer is of the major reasons of human death universally, one of the deadliest types of cancer is lung cancer, that causes the highest rate of the dead in both genders combined. Detecting lung cancer in early stage does not guarantee the survive of the patient’s life but it can reduce the mortality ratio by a high degree, early detection mainly includes screening unhealthy human’s lung using most valuable imaging modality which is CT scan. Classifying nodules in lung CT images adopting an automatic computer system become a necessary task due to a huge number of situations every day to help human expert’s in decision making procedure. Over the past few years, a numerous computer system is presented, each done a certain task such as detecting, segmenting, and classifying lung tumors using dissimilar algorithms. The objective of this study is to design an automated lung nodule classification system using two distinct deep learning architectures which are Network In Network (NIN) and standard Convolution Neural Network (CNN). The two models are trained and tested using 13,500 2D cubes around the nodule location that obtained from LUNA16 dataset, the database consists of 888 3D CT scans with annotation file determined a nodule position in every scan. The models are trained with a diverse cube size and hyperparameters in order to develop a high-performance structure for each model. The experimental results showed that best achieved scores for NIN are accuracy 90%, precision 99%, recall 68%, and false positive rate 0.06%, but for the typical CNN are accuracy 90%, precision 85%, recall 85%, and false positive rate 7.52%.

Key Words:  lung cancer, deep learning, convolutional neural network, LUNA16, classification


    References

[1] F. Bray, J. Ferlay, L. A. T. Isabelle Soerjomataram, Rebecca L. Siegel, and A. Jemal, "Incidence and survival in sarcoma in the United States: A focus on musculoskeletal lesions”, CA. Cancer J. Clin. Vol. 68, No. 6, pp. 394–424. (2018).

[2] H. Tang, D. R. Kim, and X. Xie, "Automated pulmonary nodule detection using 3D deep convolutional neural network", in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), No. Isbi, pp. 523–526. (2018).

[3] J. Lyu, S. H. Ling, and S. Member, "Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images", in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 686–689. (2018).

[4] H. M. Orozco, O. O. V. Villegas, L. O. Maynez, V. G. C. Sanchez, and H. D. J. O. Dominguez, "Lung nodule classification in frequency domain using support vector machines", 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012, pp. 870–875. (2012).

[5] P. Eskandarian and J. Bagherzadeh, "Computer-aided detection of Pulmonary Nodules based on SVM in thoracic CT images", 7th Conference on Information and Knowledge Technology, IKT 2015, pp. 1–6. (2015).

[6] Gulshan V, Peng L, Coram M et al, "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathyin Retinal Fundus Photographs". JAMA. Vol. 316, pp. 2402–2410. (2016).

[7] Esteva A, Kuprel B, Novoa RA et al, "Dermatologist-Level Classification of Skin Cancer With Deep Neural Networks", Nature. Vol. 542, pp. 115–118. (2017).

[8] W. Li, P. Cao, D. Zhao, and J. Wang, "Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images", Comput. Math. Methods Med. pp.1-7. (2016).

[9] Q. Song, L. Zhao, X. Luo, and X. Dou, "Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images", J. Healthc. Eng. Vol. 2017. (2017).

[10] R. Paul, L. Hall, D. Goldgof, M. Schabath, and R. Gillies, "Predicting Nodule Malignancy using a CNN Ensemble Approach", in 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. (2018).

[11] R. Yamashita, M. Nishio, R. K. G. Do, K. Togashi, "Convolutional neural networks: an overview and application in radiology", Insights into imaging ,Vol. 9, No. 4, pp. 611–629. (2018).

[12] S. G. Armato 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. Vol. 38, No. 2, pp. 915–931. (2011). 

[13]A. A. A. Setio et al., "Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge”, Med. Image Anal. Vol. 42, pp. 1–13. (2017).

[14] M. Lin, Q. Chen, and S. Yan, "Network In Network”, Neural Evol. Comput., pp. 1–10. (2013).

[15] R. S. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, "Dropout: A Simple Way to Prevent Neural Networks from Overfittin”, J. Mach. Learn. Res. Vol. 15, pp. 1929−1958. (2014).