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Journal of Medical Systems

, 43:21 | Cite as

An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification

  • D. PalaniEmail author
  • K. Venkatalakshmi
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

Abstract

In this paper, we propose a new Internet of Things (IoT) based predictive modelling by using fuzzy cluster based augmentation and classification for predicting the lung cancer disease through continuous monitoring and also to improve the healthcare by providing medical instructions. Here, the fuzzy clustering method is used and which is based on transition region extraction for effective image segmentation. Moreover, Fuzzy C-Means Clustering algorithm is used to categorize the transitional region features from the feature of lung cancer image. In this work, Otsu thresholding method is used for extracting the transition region from lung cancer image. Moreover, the right edge image and the morphological thinning operation are used for enhancing the performance of segmentation. In addition, the morphological cleaning and the image region filling operations are performed over an edge lung cancer image for getting the object regions. In addition, we also propose a new incremental classification algorithm which combines the existing Association Rule Mining (ARM), the standard Decision Tree (DT) with temporal features and the CNN. The experiments have been conducted by using the standard images that are collected from database and the current health data which are collected from patient through IoT devices. The results proved that the performance of the proposed prediction model which is able to achieve the better accuracy when it is compared with other existing prediction model.

Keywords

Transitional image extraction Edge detection Segmentation Fuzzy C-means clustering Lung cancer image Association rule mining (ARM) Decision tree (DT) Incremental classification Convolutional neural network (CNN) Internet of things (IoT) 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflict of interest.

(In Case Animals Were Involved) Ethical Approval

Animals were not involved.

(And/or in Case Humans Were Involved) Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    World health organization cancer fact sheets. <http://www.who.int/mediacentre/factsheets/fs297/en/index.html>.
  2. 2.
    Siegel, R., Miller, K., and Jemal, A., Cancer statistics, 2016. CA-Cancer J. Clin. 66:7–30, 2016.PubMedGoogle Scholar
  3. 3.
    Aerts, H., Velazquez, E., Leijenaar, R., Parmar, C., Grossmann, P., Carvalho, S. et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5:4006, 2014.PubMedPubMedCentralGoogle Scholar
  4. 4.
    Kubota, T., Jerebko, A., Dewan, M., Salganicoff, M., and Krishnan, A., Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med. Image Anal. 15:133–154, 2011.PubMedGoogle Scholar
  5. 5.
    Sharma, N., and Aggarwal, L., Automated medical image segmentation techniques. J. Med. Phys. 35:3–14, 2010.PubMedPubMedCentralGoogle Scholar
  6. 6.
    Farag, A. A., El Munim, H. E. A., Graham, J. H., and Farag, A. A., A novel approach for lung nodules segmentation in chest ct using level sets. IEEE Trans. Image Process. 22:5202–5213, 2013.PubMedGoogle Scholar
  7. 7.
    Lassen, B., Jacobs, C., Kuhnigk, J., van Ginneken, B., and van Rikxoort, E., Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans. Phys. Med. Biol. 60:1307, 2015.PubMedGoogle Scholar
  8. 8.
    Fukushima, K., Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4):193–202, 1980.PubMedGoogle Scholar
  9. 9.
    Jebadurai, J., and Dinesh Peter, J., Super-resolution of retinal images using multi-kernel SVR for IoT healthcare applications. Futur. Gener. Comput. Syst., Elsevier 83:338–346, 2018.Google Scholar
  10. 10.
    Salunke, P., and Nerkar, R., IoT driven healthcare system for remote monitoring of patients. J. Modern Trend Sci. Technol. 3(06):100–103, 2017.Google Scholar
  11. 11.
    Gope, P., and Hwang, T., BSN-care: A secure IoT-based modern healthcare system using body sensor network. IEEE Sens. J. 16(5):1368–1376, 2016.Google Scholar
  12. 12.
    Kannan, S. R., Sathya, A., Ramathilagam, S., and Devi, R., Novel segmentation algorithm in segmenting medical images. J. Syst. Soft. 83(12):2487–2495, 2010.Google Scholar
  13. 13.
    Nan, B., Che, L., Chui, K., Chang, S., and Ong, S. H., Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput. Biol. Med. 41(1):1–10, 2011.Google Scholar
  14. 14.
    Bai, P. R., Yi, Q., Lei, L., Sheng, L., Jing, H. T., Mao, L., and Cao, Y., A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation. Comput. Biol. Med. 43(11):1827–1832, 2013.PubMedGoogle Scholar
  15. 15.
    Manikandan, S., Ramar, K., Willjuice Iruthayarajan, M., and Srinivasagan, K. G., Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm. Measurement 47:558–568, 2014.Google Scholar
  16. 16.
    Torbati, N., Ayatollahi, A., and Kermani, A., An efficient neural network based method for medical image segmentation. Comput. Biol. Med. 44:76–87, 2014.PubMedGoogle Scholar
  17. 17.
    Li, Y., Jiao, L., Shang, R., and Stolkin, R., Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Inf. Sci. 294:408–422, 2015.Google Scholar
  18. 18.
    Zhang, X., Li, X. f., and Feng, Y., A medical image segmentation algorithm based on bi-directional region growing. Optik Int. J. Light Electron Opt. 126(20):2398–2404, 2015.Google Scholar
  19. 19.
    Mahapatra, D., Combining multiple expert annotations using semi-supervised learning and graph cuts for medical image segmentation. Comput. Vis. Image Underst. 151:114–123, 2016.Google Scholar
  20. 20.
    De, S., Bhattacharyya, S., and Dutta, P., Automatic magnetic resonance image segmentation by fuzzy intercluster hostility index based genetic algorithm: An application. Appl. Soft Comput. 47:669–683, 2016.Google Scholar
  21. 21.
    Biswas, S., Ghoshal, D., and Hazra, R., A new algorithm of image segmentation using curve fitting based higher order polynomial smoothing. Optik 127(20):8916–8925, 2016.Google Scholar
  22. 22.
    Ghosh, P., Mitchell, M., Tanyi, J. A., and Hung, A. Y., Incorporating priors for medical image segmentation using a genetic algorithm. Neurocomputing 195(26):181–194, 2016.Google Scholar
  23. 23.
    Kalshetti, P., Bundele, M., Rahangdale, P., Jangra, D., Chattopadhyay, C., Harit, G., and Elhence, A., An interactive medical image segmentation framework using iterative refinement. Comput. Biol. Med. 83:22–33, 2017.PubMedGoogle Scholar
  24. 24.
    Zhou, S., Wang, J., Zhang, M., Cai, Q., and Gong, Y., Correntropy-based level set method for medical image segmentation and bias correction. Neurocomputing 234(19):216–229, 2017.Google Scholar
  25. 25.
    Chen, Y.-T., A novel approach to segmentation and measurement of medical image using level set methods. Magn. Reson. Imaging 39:175–193, 2017.PubMedGoogle Scholar
  26. 26.
    Yang, S.-C., A robust approach for subject segmentation of medical images: Illustration with mammograms and breast magnetic resonance images. Comput. Electr. Eng. 62:151–165, 2017.Google Scholar
  27. 27.
    Khanfir Kallel, I., Almouahed, S., Solaiman, B., and Bossé, É., An iterative possibilistic knowledge diffusion approach for blind medical image segmentation. Pattern Recogn. 78:182–197, 2018.Google Scholar
  28. 28.
    Zheng, Q., Li, H., Fan, B., Wu, S., and Xu, J., Integrating support vector machine and graph cuts for medical image segmentation. J. Vis. Commun. Image Represent. 55:157–165, 2018.Google Scholar
  29. 29.
    Liu, C., Ng, M. K.-P., and Zeng, T., Weighted variational model for selective image segmentation with application to medical images. Pattern Recogn. 76:367–379, 2018.Google Scholar
  30. 30.
    Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Adrian, A. T., Yoshu, R., Pal, B. C., and Kadoury, S., Learning normalized inputs for iterative estimation in medical image segmentation. Med. Image Anal. 44:1–13, 2018.PubMedGoogle Scholar
  31. 31.
    Zhao, W., Xu, X., Zhu, Y., and Xu, F., Active contour model based on local and global Gaussian fitting energy for medical image segmentation. Optik 158:1160–1169, 2018.Google Scholar
  32. 32.
    Vardhana, M., Arunkumar, N., Lasrado, S., Abdulhay, E., and Ramirez-Gonzalez, G., Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cogn. Syst. Res. 50:10–14, 2018.Google Scholar
  33. 33.
    Miao, J., Huang, T.-Z., Zhou, X., Wang, Y., and Liu, J., Image segmentation based on an active contour model of partial image restoration with local cosine fitting energy. Inf. Sci. 447:52–71, 2018.Google Scholar
  34. 34.
    Singh, C., and Bala, A., A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images. Appl. Soft Comput. 68:447–457, 2018.Google Scholar
  35. 35.
    Rangaswamy, C., Raju, G. T., and Seshikala, G., Novel approach for lung image segmentation through enhanced fuzzy C-means algorithm. Int. J. Pure Appl. Math. 117(21):455–465, 2017.Google Scholar
  36. 36.
    Parida, P., and Bhoi, N., Transition region based single and multiple object segmentation of gray scale images. Eng Sci. Technol. Int. J. 19(3):1206–1215, 2016.Google Scholar
  37. 37.
    Cai, W., Chen, S., and Zhang, D., Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn. 40(3):825–838, 2007.Google Scholar
  38. 38.
    Otsu, N., A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9(1):62–66, 1979.Google Scholar
  39. 39.
    Sun, S., Bauer, C., and Beichel, R., Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach. IEEE Trans. Med Imaging 31(2):449–460, 2012.PubMedGoogle Scholar
  40. 40.
    Taher, F., and Sammouda, R., Lung cancer detection by using artificial neural network and fuzzy clustering methods. IEEE GCC Conf. Exhib. 10:295–298, 2011.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringUniversity College of Engineering VillupuramVillupuramIndia
  2. 2.Department of Electronics and Communication EngineeringUniversity College of Engineering TindivanamTindivanamIndia

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