Skip to main content

Advertisement

SpringerLink
Log in

A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Data directly used in this research cannot be made publicly available due to the privacy concerns of the patients. The corresponding author may provide data about the study’s findings upon reasonable request.

References

  1. Marvin IS, Talmadge E. King-Google Books. 2021. https://books.google.co.in/books?hl=en&lr=&id=xcN2AwAAQBAJ&oi=fnd&pg=PR1&dq=interstitial+lung+disease&ots=0zqs8hSpOp&sig=SyuCbXJXLtbgINAaRZf3LZ3AB9Y&redir_esc=y#v=onepage&q=interstitiallungdisease&f=false. Accessed 16 Jul 2021.

  2. Crystal RG, Gadek JE, Ferrans VJ, Fulmer JD, Line BR, Hunninghake GW. Interstitial lung disease: current concepts of pathogenesis, staging and therapy. Am J Med. 1981;70(3):542–68. https://doi.org/10.1016/0002-9343(81)90577-5.

    Article  Google Scholar 

  3. Antoniou KM, Margaritopoulos GA, Tomassetti S, Bonella F, Costabel U, Poletti V. Interstitial lung disease. Eur Respir Rev. 2014;23(131):40–54. https://doi.org/10.1183/09059180.00009113.

    Article  Google Scholar 

  4. Wells AU, Hirani N. Interstitial lung disease guideline. Thorax. 2008;63(Suppl 5):v1–58. https://doi.org/10.1136/THX.2008.101691.

    Article  Google Scholar 

  5. Wells AU. The revised ATS/ERS/JRS/ALAT diagnostic criteria for idiopathic pulmonary fibrosis (IPF)–practical implications. Respir Res. 2013;14(suppl 1):S2.

    Article  Google Scholar 

  6. Fernandez Perez ER, Daniels CE, Schroeder DR, et al. Incidence, prevalence, and clinical course of idiopathic pulmonary fibrosis: a population-based study. Chest. 2010;137:129–37.

    Article  Google Scholar 

  7. Collard HR, King TE Jr, Bartelson BB, Vourlekis JS, Schwarz MI, Brown KK. Changes in clinical and physiologic variables predict survival in idiopathic pulmonary fibrosis. Am J RespirCrit Care Med. 2003;168:538–42.

    Article  Google Scholar 

  8. Flaherty KR, King TE Jr, Raghu G, et al. Idiopathic interstitial pneumonia: what is the effect of a multidisciplinary approach to diagnosis? Am J RespirCrit Care Med. 2004;170:904–10.

    Article  Google Scholar 

  9. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;103(3):257–73. https://doi.org/10.1007/S12194-017-0406-5.

    Article  Google Scholar 

  10. Lee J-G, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017;18(4):570–84. https://doi.org/10.3348/KJR.2017.18.4.570.

    Article  Google Scholar 

  11. McAdams HP, Samei E, Dobbins J III, et al. Recent advances in chest radiography. Radiology. 2006;241:663–83.

    Article  Google Scholar 

  12. Cicero M, Bilbily A, Colak E, et al. Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest Radiol. 2017;52:281–7.

    Article  Google Scholar 

  13. Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574–82.

    Article  Google Scholar 

  14. González G, et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med. 2018;197(2):193–203.

    Article  Google Scholar 

  15. Agarwala S, et al. Deep learning for screening of interstitial lung disease patterns in high-resolution CT images. Clin Radiol. 2020;75(6):481.e1-481.e8. https://doi.org/10.1016/J.CRAD.2020.01.010.

    Article  Google Scholar 

  16. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging. 2016;35(5):1207–16. https://doi.org/10.1109/TMI.2016.2535865.

    Article  Google Scholar 

  17. Trusculescu AA, Manolescu D, Tudorache E, Oancea C. Deep learning in interstitial lung disease—how long until daily practice. Eur Radiol. 2020;30(11):6285–92. https://doi.org/10.1007/S00330-020-06986-4.

    Article  Google Scholar 

  18. Park SC, et al. Computer-aided detection of early interstitial lung diseases using low-dose CT images. Phys Med Biol. 2011;56(4):1139. https://doi.org/10.1088/0031-9155/56/4/016.

    Article  Google Scholar 

  19. Christodoulidis S, Anthimopoulos M, Ebner L, Christe A, Mougiakakou S. Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Heal Informatics. 2017;21(1):76–84. https://doi.org/10.1109/JBHI.2016.2636929.

    Article  Google Scholar 

  20. Kim GB, Jung KH, Lee Y, et al. Comparison of shallow and deep learning methods on classifying the regional pattern of diffuse lung disease. J Digit Imaging. 2018;31(4):415–24.

    Article  Google Scholar 

  21. Gao M, et al. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng. 2016. https://doi.org/10.1080/21681163.2015.1124249.

    Article  Google Scholar 

  22. Wang Z, Gu S, Leader JK, et al. Optimal threshold in CT quantification of emphysema. EurRadiol. 2013;23(4):975–84.

    Google Scholar 

  23. Bae H-J, et al. A Perlin noise-based augmentation strategy for deep learning with small data samples of HRCT images. Sci Rep. 2018. https://doi.org/10.1038/s41598-018-36047-2.

    Article  Google Scholar 

  24. Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. 2018;6(11):837–45. https://doi.org/10.1016/S2213-2600(18)30286-8.

    Article  Google Scholar 

  25. Raju N, Anita HB, Augustine P. Identification of interstitial lung diseases using deep learning. Int J Electr Comput Eng. 2020;10(6):6283–91. https://doi.org/10.11591/IJECE.V10I6.PP6283-6291.

    Article  Google Scholar 

  26. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. 2021. http://code.google.com/p/cuda-convnet/. Accessed 14 Jul 2021.

  27. van Tulder G, de Bruijne M. Learning features for tissue classification with the classification restricted Boltzmann machine. Lect Notes Comput Sci. 2014;8848:47–58. https://doi.org/10.1007/978-3-319-13972-2_5. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics).

    Article  Google Scholar 

  28. Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M. Medical image classification with convolutional neural network. Int Conf Control Autom Robot Vis. 2014. https://doi.org/10.1109/ICARCV.2014.7064414.

    Article  Google Scholar 

  29. Heitmann KR. Automatic detection of ground glass opacities on lung HRCT using multiple neural networks. Eur Radiol. 1997;7(9):1463–72.

    Article  Google Scholar 

  30. Uppaluri R. Computer recognition of regional lung disease patterns. Am J Respir Crit Care Med. 1999;160(2):648–54.

    Article  Google Scholar 

  31. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.

    Article  Google Scholar 

  32. Van Tulder G, de Bruijne M. Learning features for tissue classification with the classification restricted Boltzmann machine. In: Medical computer vision: algorithms for big data. Cham: Springer; 2014. p. 47–58.

    Chapter  Google Scholar 

  33. Li Q. Medical image classification with convolutional neural network. In: International Conference on Control, Automation, Robotics and Vision, 2014, pp. 844–8.

  34. Gao M. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. In: 1st Workshop Deep Learning Medical Image Analysis, 2015, pp. 41–8.

  35. How to Configure Image Data Augmentation in Keras. 2021. https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/. Accessed 16 Jul 2021.

  36. Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning. 2017. https://arxiv.org/abs/1712.04621v1. Accessed 16 Jul 2021.

  37. Hassan A, Mahmood A. Efficient deep learning model for text classification based on recurrent and convolutional layers. Proc IEEE Int Conf Mach Learn Appl. 2017. https://doi.org/10.1109/ICMLA.2017.00009.

    Article  Google Scholar 

  38. How Do Convolutional Layers Work in Deep Learning Neural Networks? 2021. https://machinelearningmastery.com/convolutional-layers-for-deep-learning-neural-networks/. Accessed 16 Jul 2021.

  39. A Gentle Introduction to Pooling Layers for Convolutional Neural Networks. 2021. https://machinelearningmastery.com/pooling-layers-for-convolutional-neural-networks/. Accessed 16 Jul 2021.

  40. Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: PMLR, pp. 1050–1059. http://proceedings.mlr.press/v48/gal16.html. Accessed 16 Jul 2021.

  41. Garbin C, Xingquan Z, Oge M, Zhu X. Dropout vs batch normalization: an empirical study of their impact to deep learning. Multimed Tools Appl. 2020;79:12777–815. https://doi.org/10.1007/s11042-019-08453-9.

    Article  Google Scholar 

Download references

Funding

No funding received.

Author information

Authors and Affiliations

  1. Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India

    Nidhin Raju, D. Peter Augustine & H. B. Anita

Authors
  1. Nidhin Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Peter Augustine
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. B. Anita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nidhin Raju.

Ethics declarations

Conflict of Interest

No conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raju, N., Augustine, D.P. & Anita, H.B. A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images. SN COMPUT. SCI. 4, 132 (2023). https://doi.org/10.1007/s42979-022-01579-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01579-y

Keywords

Search

Navigation