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Detection of Lung Cancer from CT Images Using Image Processing

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

Abstract

Cancer is a life-threatening disease which involves abnormal cell division and invasion of such cells to other parts of the body as well. Lung cancer is medically named as Lung Carcinoma. Lung cancer may be diagnosed using Chest Radiographs or Computed Tomography (CT) scans. In order to detect the tumor accurately and more precisely we go for CT scan, which has less noise when compared to Magnetic Resonance Imaging (MRI) images. To further improve the quality and accuracy of images, Median filter and Watershed segmentation is used. MATLAB is an Image Processing Tool used to exploit a comprehensive set of standard algorithms for image processing, analysis and visualization. Here image processing techniques like pre-processing, segmentation and feature extraction are utilized to identify the exact location of the tumor in the lung using CT images as the input. This enables lung cancer to be detected and diagnosed for adequate treatment and medications without considerable time delay.

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References

  1. Kalaivani, S., Chatterjee, P., Juyal, S., Gupta, R.: Lung cancer detection using digital image processing and artificial neural networks. In: International Conference on Electronics, Communication and Aerospace Technology (ICECA) (2017)

    Google Scholar 

  2. Rahane, W., Dalvi, H., Magar, Y., Kalane, A.: Lung cancer detection using image processing and machine learning healthcare. In: IEEE International Conference on Current Trends toward Converging Technologies (ICCTCT) (2018)

    Google Scholar 

  3. Kulkarni, A., Panditrao, A.: Classification of lung cancer stages on CT scan images using image processing. In: International Conference on Advanced Communication Control and Computing Technologies (lCACCCT) (2014)

    Google Scholar 

  4. Sammouda, R.: Segmentation and analysis of CT chest images for early lung cancer detection. In: Global Summit on Computer & Information Technology (2016)

    Google Scholar 

  5. Avinash, S., Manjunath, K., Senthil Kumar, S.: 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 (2016)

    Google Scholar 

  6. Al-Tarawneh, M.S.: Lung cancer detection using image processing techniques. Leonardo Electron. J. Pract. Technol. 11(21), 147–158 (2012)

    Google Scholar 

  7. Pandey, N., Nandy, S.: A novel approach of cancerous cells detection from lungs CT scan images. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(8), 316–320 (2012)

    Google Scholar 

  8. Chaudhary, A., Singh, S.S.: Lung cancer detection on CT images using image processing. In: International Transaction on Computing Sciences, vol. 4 (2012)

    Google Scholar 

  9. Bandyopadhyay, S.K.: Edge detection from CT images of lung. Int. J. Eng. Sci. Adv. Technol. 2(1), 34–37 (2012)

    Article  Google Scholar 

  10. Kaur, A.R.: Feature extraction and principal component analysis for lung cancer detection in CT scan images. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(3), 187–190 (2013)

    Google Scholar 

  11. Hadavi, N., Nordin, M.J., Shojaeipour, A.: Lung cancer diagnosis using CT scan images based on cellular learning automata. In: International Conference on Computer and Information Sciences. IEEE (2014)

    Google Scholar 

  12. Vijaya, G., Suhasini, A., Priya, R.: Automatic detection of lung cancer in CT images. Int. J. Res. Eng. Technol. 3(7), 182–186 (2014)

    Google Scholar 

  13. Miah, M.B.A., Yousuf, M.A.: Detection of Lung cancer from CT image using image processing and neural network. In: 2nd International Conference on Electrical Engineering and Information and Communication Technology (2015)

    Google Scholar 

  14. Agarwal, R., Shankhadhar, A., Sagar, R.K.: Detection of lung cancer using content based medical image retrieval. In: 5th International Conference on Advanced Computer and Communication Technologies, pp. 48–52 (2015)

    Google Scholar 

  15. Deshpande, A.S., Lokhande, D.D., Mundhe, R.P., Ghatole, J.M.: Lung cancer detection with fusion of CT and MRI images using image processing and machine learning. Int. J. Adv. Res. Comput. Eng. Technol. 4(3), 763–767 (2015)

    Google Scholar 

  16. Mahersia, H., Zaroug, M., Gabralla, L.: Lung cancer detection on CT scan images: a review on the analysis techniques. Int. J. Adv. Res. Artif. Intell. 4(4), 38–45 (2015)

    Article  Google Scholar 

  17. Pratap, G.P., Chauhan, R.P.: Detection of lung cancer cells using image processing techniques. In: 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems. IEEE (2016)

    Google Scholar 

  18. Rossetto, A.M., Zhou, W.: Deep learning for categorization of lung cancer CT images. In: IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (2017)

    Google Scholar 

  19. Amandeep Kaur, A.: Image segmentation using watershed transform. Int. J. Soft Comput. Eng. 4(1), 5–8 (2014)

    Google Scholar 

  20. Lu, N., Ke, X.Z.: A segmentation method based on gray-scale morphological filter and watershed algorithm for touching objects image. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery (2007)

    Google Scholar 

  21. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Hoboken (2002)

    Google Scholar 

  22. Jemimah, C., Lilly Sheeba, S.: Analysis of bodily fluids and fomites in transmission of ebola virus using bigdata. Procedia Comput. Sci. 92, 56–62 (2016)

    Article  Google Scholar 

  23. Varela-Santos, S., Melin, P.: A new modular neural network approach with fuzzy response integration for lung disease classification based on multiple objective feature optimization in chest X-ray images. Expert Syst. Appl. 168, 114361 (2021)

    Article  Google Scholar 

  24. Varela-Santos, S., Melin, P.: A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf. Sci. 545, 403–414 (2021)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India

    S. Lilly Sheeba

  2. Anna University, Chennai, India

    L. Gethsia Judin

Authors
  1. S. Lilly Sheeba
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  2. L. Gethsia Judin
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Corresponding author

Correspondence to S. Lilly Sheeba .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Lilly Sheeba, S., Gethsia Judin, L. (2022). Detection of Lung Cancer from CT Images Using Image Processing. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_64

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