Skip to main content
SpringerLink
Log in

Lung Cancer Detection Using Textural Feature Extraction and Hybrid Classification Model

  • Conference paper
  • First Online:
Proceedings of Third International Conference on Computing, Communications, and Cyber-Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 421))

Abstract

Medical image processing (MIP) offers powerful and promising key developments in modernistic three-dimensional (3D) medical imaging based on science and medicine due to the creation of hi-tech images. Image processing is used to detect lung cancer. Detecting a cancer nodule consists of three levels. CT scans are generally adopted to identify the incidence of cancer affected nodules. To improve the interpretation of information in an image to a human audience, the step of image enhancement is enforced. The next step of segmentation involves segmenting the required area into many sub-areas. The output of this step is used as input for the next step of feature extraction. Cancer, at this stage, is detected on the basis of the abstracted features. This work implements GLCM with a hybrid classifier model to localize and classify the cancer affected area from the CT scan. The hybrid classifier framework constructed by integrating KNN, SVM, and decision tree classifiers is an efficient cancer detection framework work. This work takes three parameters (i.e., accuracy, precision, and recall) under consideration to evaluate the designed hybrid classifier model.

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

Access this chapter

Log in via an institution
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

References

  1. Roy, K., Chaudhury, S. S., Burman, M., Ganguly, A., Dutta, C., Banik, S., & Banik, R. (2019). A comparative study of lung cancer detection using supervised neural network. In International Conference on Opto-Electronics and Applied Optics (Optronix).

    Google Scholar 

  2. Jony, M. H., Johora, F. T., Khatun, P., & Rana, H. K. (2019). Detection of lung cancer from CT scan images using GLCM and SVM. In 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT).

    Google Scholar 

  3. Günaydin, Ö., Günay, M., & Şengel, Ö. (2019). Comparison of lung cancer detection algorithms. In Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT).

    Google Scholar 

  4. Devarapalli, R. M., Kalluri, H. K., & Dondeti, V. (2019). Lung cancer detection of CT lung images. International Journal of Recent Technology and Engineering (IJRTE).

    Google Scholar 

  5. Shukla, A., Parab, C., Patil, P., & Sangam, S. (2018). Lung cancer detection using image processing techniques. International Research Journal of Engineering and Technology (IRJET).

    Google Scholar 

  6. Mithuna, B.N., Ravikumar, P., & Arpitha, C. N. (2018). A quantitative approach for determining lung cancer using CT scan images. In Second International Conference on Electronics, Communication and Aerospace Technology (ICECA)

    Google Scholar 

  7. Lobo, P., & Guruprasad, S. (2018). Classification and segmentation techniques for detection of lung cancer from CT images. In International Conference on Inventive Research in Computing Applications (ICIRCA).

    Google Scholar 

  8. Makaju, S., Prasad, P. W. C., Alsadoon, A., Singh, A. K., Elchouemi, A. (2018). Lung cancer detection using CT scan images. Procedia Computer Science.

    Google Scholar 

  9. Kaucha, D. P., Prasad, P. W. C., Alsadoon, A., Elchouemi, A., & Sreedharan, S. (2017). Early detection of lung cancer using SVM classifier in biomedical image processing. In IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)

    Google Scholar 

  10. Anifah, L., Harimurti, R., Permatasari, Z., Rusimamto, P. W., & Muhamad, A. R. (2017). Cancer lungs detection on CT scan image using artificial neural network backpropagation based gray level co-occurrence matrices feature. In International Conference on Advanced Computer Science and Information Systems (ICACSIS).

    Google Scholar 

  11. Fule, S. (2017). Lung cancer detection using image processing techniques. International Research Journal of Engineering and Technology (IRJET).

    Google Scholar 

  12. Abdillah, B., Bustamam, A., & Sarwinda, D. (2016). Image processing-based detection of lung cancer on CT scan images. In The Asian Mathematical Conference.

    Google Scholar 

  13. Dhaware, B. U., & Pise, A. C. (2016). Lung cancer detection using Bayesian classifier and FCM segmentation. In International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT).

    Google Scholar 

  14. Avinash, S., Manjunath, K., & Senthil Kumar, S. (2016). An improved image processing analysis for the detection of lung cancer using Gabor filters and watershed segmentation technique. In International Conference on Inventive Computation Technologies (ICICT).

    Google Scholar 

  15. Usman, M., Shoaib, M., & Rahal, M. (2013). Lung cancer detection using digital image processing. In PIERS Proceedings, Stockholm, Sweden.

    Google Scholar 

  16. Al-Tarawneh, M. S. (2012). Lung cancer detection using image processing techniques. Leonardo Electronic Journal of Practices and Technologies.

    Google Scholar 

  17. Chaudhary, A., & Singh, S. S. (2012). Multiresolution analysis technique for lung cancer detection in computed tomographic images. International Journal of Research in Engineering & Applied Sciences, IJREAS.

    Google Scholar 

  18. Al-Tarawneh, F. S. (2012). Lung cancer detection using image processing techniques. Leonardo Electronic Journal of Practices and Technologies.

    Google Scholar 

  19. Bandyopadhyay, S. K. (2012). Edge detection from CT images of lung. International Journal of Engineering Science & Advanced Technology.

    Google Scholar 

  20. 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).

    Google Scholar 

  21. Anifah, L., Harimurti, R., Permatasari, Z., Rusimamto, P. W., & Muhamad, A. R. (2017). Cancer lungs detection on CT scan image using artificial neural network backpropagation based gray level coocurrence matrices feature. In International Conference on Advanced Computer Science and Information Systems (ICACSIS).

    Google Scholar 

  22. Chunran, Y., Yuanvuan, W., & Yi, G. (2018). Automatic detection and segmentation of lung nodule on CT images. In 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

    Google Scholar 

  23. Jin, T., Cui, H., Zeng, S., & Wang, X. (2017). Learning deep spatial lung features by 3D convolutional neural network for early cancer detection. In International Conference on Digital Image Computing: Techniques and Applications (DICTA).

    Google Scholar 

  24. Krishna, A., Srinivasa Rao, P.C., & Basha, C. Z. (2020). Efficient computerized lung cancer detection using bag of words. In 7th International Conference on Smart Structures and Systems (ICSSS).

    Google Scholar 

  25. Wu, Q., & Zhao, W. (2017). Small-cell lung cancer detection using a supervised machine learning algorithm. In International Symposium on Computer Science and Intelligent Controls (ISCSIC).

    Google Scholar 

  26. Alam, J., Alam, S., & Hossan, A. (2018). Multi-stage lung cancer detection and prediction using multi-class SVM classifier. In International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2).

    Google Scholar 

  27. Khosravan, N., & Bagci, U. (2018). Semi-supervised multi-task learning for lung cancer diagnosis. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

    Google Scholar 

  28. Vas, M., & Dessai, A. (2017). In International Conference on Computing, Communication, Control and Automation (ICCUBEA)

    Google Scholar 

  29. Firdaus, Q., Sigit, R., Harsono, T., & Anwar, A. (2020). Lung cancer detection based on CT-scan images with detection features using gray level co-occurrence matrix (GLCM) and support vector machine (SVM) methods. In International Electronics Symposium (IES)

    Google Scholar 

  30. Huidrom, R., Chanu, Y. J., Singh, K. M. (2017). A fast automated lung segmentation method for the diagnosis of lung cancer. In IEEE Region 10 Conference.

    Google Scholar 

  31. Hoque, A., Ashek Farabi, A. K. M., Ahmed, F., & Islam, M. Z. (2020). Automated detection of lung cancer using CT scan images. In IEEE Region 10 Symposium (TENSYMP).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chandigarh University, Mohali, Punjab, India

    Jasbir Kaur & Meenu Gupta

Authors
  1. Jasbir Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meenu Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meenu Gupta .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KIET Group of Institutions, Ghaziabad, Delhi, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Sławomir T. Wierzchoń

  3. Department of Computer Science and Engineering, Nirma University, Ahmedabad, India

    Sudeep Tanwar

  4. Federal University of Piauí, Teresina, Brazil

    Joel J. P. C. Rodrigues

  5. Faculty of Mathematics and Informatics, Warsaw University of Technology, Warsaw, Poland

    Maria Ganzha

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Kaur, J., Gupta, M. (2023). Lung Cancer Detection Using Textural Feature Extraction and Hybrid Classification Model. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-19-1142-2_65

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-1142-2_65

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1141-5

  • Online ISBN: 978-981-19-1142-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation