Skip to main content
SpringerLink
Log in

Computer-Aided Detection and Diagnosis of Lung Nodules Using CT Scan Images: An Analytical Review

  • Conference paper
  • First Online:
Proceedings of Second Doctoral Symposium on Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1374))

  • 1289 Accesses

Abstract

Cancer is one of the leading causes of mortality worldwide, lung cancer being one of the deadliest. Early detection and accurate diagnosis of lung nodules can save many lives and resources. Number of diagnostic radiology utilized for detection of lung nodules of which computed tomography (CT) scans provide better discernment of disease, thus explored extensively for the automatic nodule analysis. However, manual analysis of radiological images is time-consuming and prone to human errors like detection and interpretation errors. On the other hand, computer-aided detection and diagnosis (CAD) system eliminates manual process and problems associated with it. In this work, an analytical review on various CAD systems for detection and characterization of lung nodules using CT scan images is discussed. A detailed structure of each component of CAD system is presented. Diverse CAD systems which are developed on the basis of state-of-the-art convolutional neural networks (CNN), such as 3D-CNN, transferable CNN, dense convolutional binary tree network, gated dilated network, and mask region CNN, are addressed. The algorithms performance is compared based on metrics: sensitivity (SEN), accuracy (ACC), area under curve (AUC), etc. In order to develop more robust end-to-end system, coupling between detection and diagnostic components is also explored. Finally, current challenges faced in analysis and characterization of lung nodule by the present system and future research opportunities in this field are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. WHO Report on Cancer, Setting Priorities, Investing Wisely and Providing Care for All,World Health Organization, Geneva, Switzerland. (2020).

    Google Scholar 

  2. Ozdemir, O., Russell, R. L., & Berlin, A. A. (2020). A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans. IEEE Transactions on Medical Imaging, 39(5), 1419–1429. https://doi.org/10.1109/TMI.2019.2947595

    Article  Google Scholar 

  3. Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. Cancer Journal for Clinicians, 70(1), 7–30. https://doi.org/10.3322/caac.21590

  4. De Koning, H. J. (2020). Reduced lung-cancer mortality with volume CT screening in a randomized trial. New England Journal of. Medicine, 382(6), 503–513. https://doi.org/10.1056/nejmoa1911793

  5. Snoeckx, A., Reyntiens, P., Desbuquoit, D., Spinhoven, M. J., Van Schil, P. E., Meerbeeck, J. P., & Parizel, P. M. (2018). Evaluation of the solitary pulmonary nodule: Size matters, but do not ignore the power of morphology. Insights into Imaging, 9(1), 73–86.

    Google Scholar 

  6. Zhou, Q. (2016). China national guideline of classification, diagnosis and treatment for lung nodules. Zhongguo Zhi, 19(12), 793–798. https://doi.org/10.3779/j.issn.1009-3419.2016.12.12

  7. Cressman, S. (2017). The cost-effectiveness of high-risk lung cancer screening and drivers of program efficiency. Journal of Thoracic Oncology, 12(8), 1210–1222. https://doi.org/10.1016/j.jtho.2017.04.021

  8. Gu, J., Tian, Z., & Qi, Y. (2020). Pulmonary nodules detection based on deformable convolution. IEEE Access, 8, 16302–16309. https://doi.org/10.1109/ACCESS.2020.2967238

    Article  Google Scholar 

  9. Hussein, S., Kandel, P., Bolan, C. W., Wallace, M. B., & Bagci, U. (2019). Lung and pancreatic tumour characterization in the deep learning era: Novel supervised and unsupervised learning approaches. IEEE Transactions on Medical Imaging, 38(8), 1777–1787. https://doi.org/10.1109/TMI.2019.2894349

    Article  Google Scholar 

  10. Tong, C., et al. (2021). Pulmonary nodule classification based on heterogeneous features learning. IEEE Journal on Selected Areas in Communications, 39(2), 574–581. https://doi.org/10.1109/JSAC.2020.3020657

    Article  Google Scholar 

  11. Ye, Y., Tian, M., Liu, Q., & Tai, H.-M. (2020). Pulmonary nodule detection using v-net and high-level descriptor based SVM classifier. IEEE Access, 8, 176033–176041. https://doi.org/10.1109/ACCESS.2020.3026168

    Article  Google Scholar 

  12. Ali, I., Muzammil, M., Haq, I. U., Khaliq, A. A., & Abdullah, S. (2020). Efficient lung nodule classification using transferable texture convolutional neural network. IEEE Access, 8, 175859–175870. https://doi.org/10.1109/ACCESS.2020.3026080

    Article  Google Scholar 

  13. Gong, L., Jiang, S., Yang, Z., et al. (2019). Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks. International Journal of Computer Assisted Radiology and Surgery, 14, 1969–1979. https://doi.org/10.1007/s11548-019-01979-1

    Article  Google Scholar 

  14. Su, Y., Li, D., & Chen, X. (2020). Lung nodule detection based on faster R-CNN framework. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2020.105866

    Article  Google Scholar 

  15. Cai, L., Long, T., Dai, Y., & Huang, Y. (2020). Mask R-CNN-based detection and segmentation for pulmonary nodule 3D visualization diagnosis. IEEE Access, 8, 44400–44409. https://doi.org/10.1109/ACCESS.2020.2976432

    Article  Google Scholar 

  16. Harsono, I. W., Liawatimena, S., & Cenggoro, T. W. (2020). Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning. Journal King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.03.013. ISSN 1319-1578.

  17. Shi, Y., Li, H., Zhang, H., Wu, Z., & Ren, S. (2020). Accurate and efficient LIF-Nets for 3D detection and recognition. IEEE Access, 8, 98562–98571. https://doi.org/10.1109/ACCESS.2020.2995886

    Article  Google Scholar 

  18. Masood, A., et al. (2020). Automated decision support system for lung cancer detection and classification via enhanced RFCN with multilayer fusion RPN. IEEE Transactions on Industrial Informatics, 16(12), 7791–7801. https://doi.org/10.1109/TII.2020.2972918

    Article  Google Scholar 

  19. Kuo, C-F. J., Huang, C-C., Siao, J-J., Hsieh, C-W., Huy, V. Q., Ko, K-H., & Hsu, H-H. (2020). Automatic lung nodule detection system using image processing techniques in computed tomography. Biomedical Signal Processing and Control, 56, 101659. https://doi.org/10.1016/j.bspc.2019.101659. ISSN 1746-8094.

  20. Rey, A., Arcay, B., & Castro, A. (2020). A hybrid CAD system for lung nodule detection using CT studies based in soft computing. Expert Systems with Applications, 114259. https://doi.org/10.1016/j.eswa.2020.114259. ISSN 0957-4174.

  21. Zheng, S., Cui, X., Vonder, Raymond, M., Veldhuis, N. J., Ye, Z., Vliegenthart, R., Oudkerk, M., & van Ooijen, P. M. A. (2020). Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage. Computer Methods and Programs in Biomedical, 196, 105620. https://doi.org/10.1016/j.cmpb.2020.105620. ISSN 0169-2607.

  22. Suresh, S., & Mohan, S. (2019). NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2019.11.013. ISSN 1319-1578.

  23. Al-Shabi, M., Lee, H. K., & Tan, M. (2019). Gated-dilated networks for lung nodule classification in CT scans. IEEE Access, 7, 178827–178838. https://doi.org/10.1109/ACCESS.2019.2958663

    Article  Google Scholar 

  24. Veasey, B. P., Broadhead, J., Dahle, M., Seow, A., & Amini, A. A. (2020). Lung nodule malignancy prediction from longitudinal CT scans with siamese convolutional attention networks. IEEE Open Journal of Engineering in Medicine and Biology, 1, 257–264. https://doi.org/10.1109/OJEMB.2020.3023614

    Article  Google Scholar 

  25. Li, X., Li, B., Liu, F., Yin, H., & Zhou, F. (2020). Segmentation of pulmonary nodules using a GMM fuzzy C-Means algorithm. IEEE Access, 8, 37541–37556. https://doi.org/10.1109/ACCESS.2020.2968936

    Article  Google Scholar 

  26. Sun, Y., Tang, J., Lei, W., & He, D. (2020). 3D Segmentation of pulmonary nodules based on multi-view and semi-supervised. IEEE Access, 8, 26457–26467. https://doi.org/10.1109/ACCESS.2020.2971542

    Article  Google Scholar 

  27. Xie, Y., et al. (2019). Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Transactions on Medical Imaging, 38(4), 991–1004. https://doi.org/10.1109/TMI.2018.2876510

    Article  Google Scholar 

  28. Li, G., et al. (2020). Study on the detection of pulmonary nodules in CT images based on deep learning. IEEE Access, 8, 67300–67309. https://doi.org/10.1109/ACCESS.2020.2984381

    Article  Google Scholar 

  29. Zhang, Q., & Kong, X. (2020). Design of automatic lung nodule detection system based on multi-scene deep learning framework. IEEE Access, 8, 90380–90389. https://doi.org/10.1109/ACCESS.2020.2993872

    Article  Google Scholar 

  30. Gu, X., Xie, W., Fang, Q., Zhao, J., & Li, Q. (2020). The effect of pulmonary vessel suppression on computerized detection of nodules in chest CT scans. Medical Physics, 47, 4917–4927. https://doi.org/10.1002/mp.14401

    Article  Google Scholar 

  31. Shaukat, F., Raja, G., Ashraf, R., et al. (2019). Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features. Journal of Ambient Intelligence and Humanized Computing, 10, 4135–4149. https://doi.org/10.1007/s12652-019-01173-w

    Article  Google Scholar 

  32. Roy, R., Banerjee, P., & Chowdhury, A. S. (2020). A level set based unified framework for pulmonary nodule segmentation. IEEE Signal Processing Letters, 27, 1465–1469. https://doi.org/10.1109/LSP.2020.3016563

    Article  Google Scholar 

  33. Samundeeswari, P., & Gunasundari, R. (2020). A novel multilevel hybrid segmentation and refinement method for automatic heterogeneous true NSCLC nodules extraction. In 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India (pp. 226–235). https://doi.org/10.1109/ICDCS48716.2020.243586

  34. Gong, J., Liu, J., Wang, L., Sun, X., Zheng, B., & Nie, S. (2018). Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. Physica Medica, 46, 124–133. https://doi.org/10.1016/j.ejmp.2018.01.019. ISSN 1120-1797.

  35. Chung, H., Ko, H., Jeon, S. J., Yoon, K. H., & Lee, J. (2018). Automatic lung segmentation with Juxta-Pleural nodule identification using active contour model and bayesian approach. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–13, 1800513. https://doi.org/10.1109/JTEHM.2018.2837901

  36. Wang, B., et al. (2020). A fast and efficient CAD system for improving the performance of malignancy level classification on lung nodules. IEEE Access, 8, 40151–40170. https://doi.org/10.1109/ACCESS.2020.2976575

    Article  Google Scholar 

  37. Gong, Z., Li, D., Lin, J., Zhang, Y., & Lam, K.-M. (2020). Towards accurate pulmonary nodule detection by representing nodules as points with high-resolution network. IEEE Access, 8, 157391–157402. https://doi.org/10.1109/ACCESS.2020.3019104

    Article  Google Scholar 

  38. Zheng, S., Guo, J., Cui, X., Veldhuis, R. N. J., Oudkerk, M., & van Ooijen, P. M. A. (2020). Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Transactions on Medical Imaging, 39(3), 797–805. https://doi.org/10.1109/TMI.2019.2935553

  39. Monkam, P., et al. (2019). Ensemble learning of multiple-view 3D-CNNs model for micro-nodules identification in CT images. IEEE Access, 7, 5564–5576. https://doi.org/10.1109/ACCESS.2018.2889350

    Article  Google Scholar 

  40. Chenyang, L., & Chan, S.-C. (2020). A joint detection and recognition approach to lung cancer diagnosis from CT images with label uncertainty. IEEE Access, 8, 228905–228921. https://doi.org/10.1109/ACCESS.2020.3044941

    Article  Google Scholar 

  41. Zhou, Z., Li, S., Qin, G., Folkert, M., Jiang, S., & Wang, J. (2020). Multi-Objective based radiomic feature selection for lesion malignancy classification. IEEE Journal of Biomedical and Health Informatics, 24(1), 194–204. https://doi.org/10.1109/JBHI.2019.2902298

    Article  Google Scholar 

  42. Khan, S. A., Nazir, M., Khan, M. A., et al. (2019). Lungs nodule detection framework from computed tomography images using support vector machine. Microscopy Research and Technique, 82, 1256–1266. https://doi.org/10.1002/jemt.23275

    Article  Google Scholar 

  43. Sahu, P., Yu, D., Dasari, M., Hou, F., & Qin, H. (2019). A lightweight multi-section CNN for lung nodule classification and malignancy estimation. IEEE Journal of Biomedical and Health Informatics, 23(3), 960–968. https://doi.org/10.1109/JBHI.2018.2879834

    Article  Google Scholar 

  44. Wang, W., et al. (2019). Nodule-Plus R-CNN and deep self-paced active learning for 3D instance segmentation of pulmonary nodules. IEEE Access, 7, 128796–128805. https://doi.org/10.1109/ACCESS.2019.2939850

    Article  Google Scholar 

  45. Saba, T., Sameh, A., Khan, F., et al. (2019). Lung nodule detection based on ensemble of hand crafted and deep features. Journal of Medical Systems, 43, 332. https://doi.org/10.1007/s10916-019-1455-6

    Article  Google Scholar 

  46. Zhang, B., et al. (2019). Ensemble learners of multiple deep CNNs for pulmonary nodules classification using CT images. IEEE Access, 7, 110358–110371. https://doi.org/10.1109/ACCESS.2019.2933670

    Article  Google Scholar 

  47. Zhai, P., Tao, Y., Chen, H., Cai, T., & Li, J. (2020). Multi-Task learning for lung nodule classification on chest CT. IEEE Access, 8, 180317–180327. https://doi.org/10.1109/ACCESS.2020.3027812

    Article  Google Scholar 

  48. Cao, H., et al. (2019). Multi-Branch ensemble learning architecture based on 3D CNN for false positive reduction in lung nodule detection. IEEE Access, 7, 67380–67391. https://doi.org/10.1109/ACCESS.2019.2906116

    Article  Google Scholar 

  49. Armato, S. G. (2011). The lung image database consortium (LIDC) and image database re-source initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics, 38(2), 915–931. https://doi.org/10.1118/1.3528204

  50. NLST Datasets. Accessed: Aug. 15, 2020. [Online]. Available: https://cdas.can-cer.gov/datasets/nlst/

  51. VIA/I-ELCAP Datasets. Accessed: Aug. 15, 2020. [Online]. Available: http://www.via.cornell.edu/databases/lungdb.html

  52. Ru Zhao, Y., Xie, X., de Koning, H. J., Mali, W. P., Vliegenthart, R., & Oudkerk, M. (2011). NELSON lung cancer screening study. Cancer Imaging, 11(1A), S79–S84. https://doi.org/10.1102/1470-7330.2011.9020

  53. Tan, M., Wu, F., Yang, B., Ma, J., Kong, D., Chen, Z., & Long, D. (2020). Pulmonary nodule detection using hybrid two-stage 3D CNNs. Medical Physics, 47, 3376–3388. https://doi.org/10.1002/mp.14161

    Article  Google Scholar 

  54. Kuang, Y., Lan, T., Peng, X., Selasi, G. E., Liu, Q., & Zhang, J. (2020). Unsupervised multi-discriminator generative adversarial network for lung nodule malignancy classification. IEEE Access, 8, 77725–77734. https://doi.org/10.1109/ACCESS.2020.2987961

    Article  Google Scholar 

  55. Masood, A., et al. (2020). Cloud-Based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT. IEEE Journal of Translational Engineering in Health and Medicine, 8, 1–13, 4300113. https://doi.org/10.1109/JTEHM.2019.2955458

Download references

Author information

Authors and Affiliations

  1. University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

    Nikhat Ali & Jyotsna Yadav

Authors
  1. Nikhat Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jyotsna Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jyotsna Yadav .

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  2. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  3. Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

    Vineet Kansal

  4. University of Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  5. Department of Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ali, N., Yadav, J. (2022). Computer-Aided Detection and Diagnosis of Lung Nodules Using CT Scan Images: An Analytical Review. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_44

Download citation

Publish with us

Policies and ethics

Search

Navigation