Skip to main content
SpringerLink
Log in

Applications of Machine Learning in Healthcare with a Case Study of Lung Cancer Diagnosis Through Deep Learning Approach

  • Chapter
  • First Online:
AI and Blockchain in Healthcare

Part of the book series: Advanced Technologies and Societal Change ((ATSC))

Abstract

The healthcare industry is growing faster as a top revenue generator from industry and offering care to the billions of people in the worldwide. The technology-based smart health care system is becoming popular where we use Internet-connected medical devices to facilitate people's needs. The technology is integrated into patient registration, billing, medical reports, and more. The machine learning approach is widely acceptable and useful in the health care system. The machine learning algorithms can be useful in identifying and diagnosing diseases, drug discovery, drug manufacturing, medical image processing, and personalized medicine treatment, behavior modification, maintaining health care records, clinical trial, data collection, and radiotherapy, and predicting epidemics around the world. This chapter will summarize the machine learning-based applications, issues, and challenges in detail. Lung diseases are affecting people globally and lung cancer is one of the leading causes of human death across the globe. The early detection of lung cancer would be helpful in increasing the survival rate. Various methods were proposed by researchers for the early detection of lung cancer in humans and most of them use CT scan images and X-ray images. In this paper, we reviewed the literature for lung cancer detection employing features using deep residual networks, and a comparison between existing techniques is presented and discussed.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book
USD 79.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Palcic, B., Lam, S., Hung, J., MacAulay, C.: Detection and localization of early lung cancer by imaging techniques. CHEST J. 99(3) 742–743 (1991)

    Google Scholar 

  2. Yamomoto, S., Jiang, H., Matsumoto, M., Tateno, Y., Iinuma, T., Matsumoto, T.: Image processing for computer-aided diagnosis of lung cancer by CT(LSCT). In: Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96, pp. 236–241 (1996). https://doi.org/10.1109/ACV.1996.572061

  3. Gurcan, M.N., Sahiner, B., Petrick, N., Chan, H.P., Kazerooni, E.A., Cascade, P.N., Hadjiiski, L.: Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med. Phys. 29(11), 2552–2558. https://doi.org/10.1118/1.1515762. PMID: 12462722

  4. Fakoor, R., Ladhak, F., Nazi, A., Huber M.: Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of the International Conference on Machine Learning (2013)

    Google Scholar 

  5. Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imag. 35(5), 1153–1159 (2016)

    Article  Google Scholar 

  6. Shen, D., Wu, G., Suk H.-I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. (2017)

    Google Scholar 

  7. Cai, Z., et al.: Classification of lung cancer using ensemble-based feature selection and machine learning methods. Molec. BioSyst. 11(3), 791–800 (2015)

    Article  Google Scholar 

  8. Al-Absi Hamada, R.H., Belhaouari Samir, B., Sulaiman, S.: A computer aided diagnosis system for lung cancer based on statistical and machine learning techniques. JCP 9(2), 425–431 (2014)

    Google Scholar 

  9. Gupta, B., Tiwari, S.: Lung cancer detection using curvelet transform and neural network. Int. J. Comput. Appl. 86(1) (2014)

    Google Scholar 

  10. Penedo, M.G., et al.: Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. IEEE Trans. Med. Imag. 17(6), 872–880 (1998)

    Article  Google Scholar 

  11. Taher, F., Sammouda, R.: Lung cancer detection by using artificial neural network and fuzzy clustering methods. In: GCC Conference and Exhibition (GCC). IEEE (2011)

    Google Scholar 

  12. Kuruvilla, J., Gunavathi, K.: Lung cancer classification using neural networks for CT images. Comput. Methods Program. Biomed. 113(1), 202–209 (2014)

    Article  Google Scholar 

  13. Shimizu, R., et al.: Deep learning application trial to lung cancer diagnosis for medical sensor systems. In: 2016 International on SoC Design Conference (ISOCC). IEEE (2016)

    Google Scholar 

  14. Hua, K.-L., et al.: Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Therapy 8, 2015–2022 (2014)

    Google Scholar 

  15. Sun, W., Zheng, B., Qian, W.: Computer aided lung cancer diagnosis with deep learning algorithms. In: SPIE Medical Imaging. International Society for Optics and Photonics (2016)

    Google Scholar 

  16. Armato, S.G., et al.: The lung image database consortium (LIDC) and image data-base resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)

    Article  Google Scholar 

  17. He, K., Zhang, X., Ren, S., Deep, S.J.: residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  18. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)

    Google Scholar 

  19. Friedman, J.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  20. Friedman, J., Hastie, T., Tibshirani, R., et al.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28(2), 337–407 (2000)

    Article  MATH  Google Scholar 

  21. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)

    Google Scholar 

  22. Aberle, D.R., Adams, A.M., Berg, C.D., Black, W.C., Clapp, J.D., Fagerstrom, R.M., Gareen, I.F., Gatsonis, C., Marcus, P.M., Sicks, J.D.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011)

    Article  Google Scholar 

  23. Moyer, V.A.: U.S. preventive services task force. Screening for lung cancer: U.S. Preventive services task force recommendation statement. Ann. Int. Med. 160, 330–338 (2014)

    Google Scholar 

  24. Lung Nodule Analysis (LUNA) Challenge. https://luna16.grand-challenge.org/description/

  25. Zatloukal, P., et al.: Concurrent versus sequential chemoradiotherapy with cisplatin and vinorelbine in locally advanced non-small cell lung cancer: a randomized study. Lung Cancer 46(1), 87–98 (2004)

    Article  Google Scholar 

  26. Clark, K., et al.: The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013)

    Article  Google Scholar 

  27. Ronneberger, O., Fischer, P., Thomas Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol. 9351, pp. 234–241. Springer, LNCS (2015)

    Google Scholar 

  28. Ciompi, F., Chung, K., van Riel, S.J., et al.: Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci. Rep. 7, 46479 (2017). [Crossref] [PubMed]

    Google Scholar 

  29. Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014). [Crossref] [PubMed]

    Google Scholar 

  30. Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al.: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012). [Crossref] [PubMed]

    Google Scholar 

  31. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. Syst. 3, 610–621 (1973). [Crossref]

    Google Scholar 

  32. Wilson, R., Devaraj, A.: Radiomics of pulmonary nodules and lung cancer. Transl. Lung Cancer Res. 6, 86–91 (2017). [Crossref] [PubMed]

    Google Scholar 

  33. Chalkidou, A., O’Doherty, M.J., Marsden, P.K.: False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One 10, e0124165 (2015). [Crossref] [PubMed]

    Google Scholar 

  34. Armato, S.G., Drukker, K., Li, F., et al.: LUNGx challenge for computerized lung nodule classification. J. Med. Imaging (Bellingham) 3, 044506 (2016). [Crossref] [PubMed]

    Google Scholar 

  35. Hammack, D.: Forecasting lung cancer diagnoses with deep learning. Available online: https://raw.githubusercontent.com/dhammack/DSB2017/master/dsb_2017_daniel_hammack.pdf

  36. Setio, A.A., Traverso, A., de Bel, T., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1–13 (2017). [Crossref] [PubMed]

    Google Scholar 

  37. Sawicki, L.M., Grueneisen, J., Buchbender, C., Schaarschmidt, B.M., Gomez, B., Ruhlmann, V., Umutlu, L., Antoch, G., Heusch, P.: Evaluation of the outcome of lung nodules missed on 18F-FDG PET/MRI compared with 18F-FDG PET/CT in patients with known Malignancies. J. Nucl. Med. 57(1), 15–20. https://doi.org/10.2967/jnumed.115.162966. Epub 2015 Oct 29. PMID: 26514173

  38. Obulesu, O., Kallam, S., Dhiman, G., Patan, R., Kadiyala, R., Raparthi, Y., Kautish, S.: Adaptive Diagnosis of lung cancer by deep learning classification using wilcoxon gain and generator. J. Healthcare Eng. Article ID 5912051, 13 (2021). https://doi.org/10.1155/2021/5912051

  39. Pradeep, K.R, Naveen, N.C.: A framework for lung cancer survivability prediction using optimized-deep neural network classification and regression technique. Int. J. Comput. Sci. Eng. 07(13), 57–66 (2019)

    Google Scholar 

Download references

Acknowledgements

I would like to express my special thanks to Jain University for supporting me for writing this chapter.

Author information

Authors and Affiliations

  1. Jain Deemed to be University, Bengaluru, India

    Taskeen Zaidi & Bijjahalli Sadanandamurthy Sushma

Authors
  1. Taskeen Zaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bijjahalli Sadanandamurthy Sushma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Taskeen Zaidi .

Editor information

Editors and Affiliations

  1. ABES Institute of Technology, Ghaziabad, India

    Bipin Kumar Rai

  2. CMR Engineering College, Hyderabad, India

    Gautam Kumar

  3. Cape Peninsula University of Technology, Cape town, South Africa

    Vipin Balyan

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zaidi, T., Sushma, B.S. (2023). Applications of Machine Learning in Healthcare with a Case Study of Lung Cancer Diagnosis Through Deep Learning Approach. In: Rai, B.K., Kumar, G., Balyan, V. (eds) AI and Blockchain in Healthcare. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-99-0377-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-0377-1_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0376-4

  • Online ISBN: 978-981-99-0377-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation