Abstract
Lung Nodules detection plays an important role to detect early stage lung cancer. Early stage lung cancer detection can considerably increases the surviving rate of patients. Radiologist diagnosis the Computerized Tomography (CT) images by detecting lung nodules. This task of locating lung nodules from CT images is rigorous and becomes even more challenging due to the structure of lung parenchyma region and also due to the size of lung nodules is small even less that 3 cm. Many Computer Aided Diagnosis CAD systems were proposed to detect lung nodules to assist radiologists. Recently, Deep learning neural network has found its way into lung nodule detection system. Deep learning neural network has shown better results and performance than traditional feature extraction based lung nodule detection techniques. This paper will focus on different deep learning neural network proposed for lung nodule detection and also we will analyze the result and performance of this detection network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
National Institute of Cancer Prevention and Research. http://cancerindia.org.in/lung-cancer/.
Sun, N., Yang, D., Fang, S., & Xie, H. (2018). Deep convolutional nets for pulmonary nodule detection and classification. In W. Liu, F. Giunchiglia, & B. Yang (Eds.), Knowledge science, engineering and management, KSEM 2018, lecture notes in computer science (Vol. 11062). Cham: Springer.
Pezeshk, A., Hamidian, S., Petrick, N., & Sahiner, B. (2018) 3D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE Journal of Biomedical and Health Informatics.
Gu, Y., Lu, X., Yang, L., Zhang, B., Yu, D., & Zhao, Y. (2018). Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Computers in Biology and Medicine, 103, 220–231.
Liu, M., Dong, J., Dong, X., Yu, H., & Qi, L. (2018). Segmentation of lung nodule in CT images based on mask R-CNN. In 9th International Conference on Awareness Science and Technology (iCAST), Fukuoka (pp. 1–6).
Jin, H., Li, Z., Tong, R., & Lin, L. (2018). A deep 3D residual CNN for false positive reduction in pulmonary nodule detection. Medical Physics, 45, 2097–2107.
Tran, G. S., Nghiem, T. P., Nguyen, V. T., Luong, C. M., & Burie, J.-C. (2019). Improving accuracy of lung nodule classification using deep learning with focal loss. Journal of Healthcare Engineering, 5156416, 9.
Sajjanar D., Rekha, B. S., & Srinivasan, G. N. (2018). Lung cancer detection and classification using convolutional neural network. Jasc Journal of Applied Science and Computations, 5(6).
Srivenkatalakshmi, R., & Balambigai, S. (2018). Lung nodule classification using deep learning algorithm. Asian Journal of Applied Science and Technology (AJAST), 2(2), 692–699.
Nóbrega, R. V. M. D., & Peixoto, S. A., Silva, S. P. P. D., & Filho, P. P. R. (2018). Lung nodule classification via deep transfer learning in CT lung images. In IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad (pp. 244–249).
Xie, Y., Xia, Y., Zhang, J., Song, Y., Feng, D., Fulham, M., & Cai, W. (2018). Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Transactions on Medical Imaging.
Fang, T. (2018) A novel computer-aided lung cancer detection method based on transfer learning from GoogLeNet and median intensity projections. In IEEE International Conference on Computer and Communication Engineering Technology (CCET), Beijing (pp. 286–290).
Naqi, S. M., Sharif, M., & Jaffar, A. (2018). Lung nodule detection and classification based on geometric fit in parametric form and deep learning. A Neural Computing and Applications.
Nam, J. G., Park, S., Hwang, E. J., Lee, J. H., Jin, K.-N., Lim, K. Y., et al. (2019). Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology, 290(1), 218–228.
Tang, H., Kim, D. R., & Xie, X. (2018) Automated pulmonary nodule detection using 3D deep convolutional neural networks. In IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC (pp. 523–526).
Winkels, M., & Cohen, T. S. (2018). 3D G-CNNs for pulmonary nodule detection. arXiv:1804.04656.
Shi, Z., Hao, H., Zhao, M., Feng, Y., He, L., Wang, Y., et al. (2018). A deep CNN based transfer learning method for false positive reduction. Multimedia Tools and Applications, 78(1), 1017.
Zhu, W., Vang, Y. S., Huang, Y., & Xie, X. (2018) Deepem: Deep 3d convnets with em for weakly supervised pulmonary nodule detection. In Medical Image Computing and Computer Assisted Intervention MICCAI.
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., et al. (2016). Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging, 35(5), 1160–1169.
Hu, Z., Muhammad, A., Zhu, M. (2018). Pulmonary nodule detection in CT images via deep neural network: Nodule candidate detection. In ICGSP’18, Proceedings of the 2nd International Conference on Graphics and Signal Processing (pp. 79–83).
Wang, Z., Xu, H., & Sun, M. (2017). Deep learning based nodule detection from pulmonary CT images. In 10th International Symposium on Computational Intelligence and Design (ISCID) (pp. 370–373), Hangzhou.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nakrani, M.G., Sable, G.S., Shinde, U.B. (2021). A Comprehensive Review on Deep Learning Based Lung Nodule Detection in Computed Tomography Images. In: Satapathy, S., Bhateja, V., Janakiramaiah, B., Chen, YW. (eds) Intelligent System Design. Advances in Intelligent Systems and Computing, vol 1171. Springer, Singapore. https://doi.org/10.1007/978-981-15-5400-1_12
Download citation
DOI: https://doi.org/10.1007/978-981-15-5400-1_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5399-8
Online ISBN: 978-981-15-5400-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)