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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.13(2) (2022)

  • Lung Cancer Nodule Detection by Using Selective Search Feature Extraction and Segmentation Approach of Deep Neural Network

    Satyasangram Sahoo , Prem Kumar Borugadda, R.Lakhsmi (Department of Computer Science, Pondicherry University, INDIA.).

    Disciplinary: Computer science, machine learning, deep neural network, image segmentation.

    ➤ FullText

    doi: 10.14456/ITJEMAST.2022.28

    Keywords: Image classification; Neural Network; Selective Search; Cancer Stage; Shape; Size; Texture; Clusterization.

    Abstract
    The study addresses the implementation of the selective search for the classification of cancer nodules in the lungs. The search processes integrate the power of both segmentation as well as exhaustive search for detection of an object in an image. In addition, the features of the cancer stage classifier are also used for cluster organization from the histogram to set the difference between inter-class variance. The selective search makes use of class variance to trace out meta-similarities. Later the neural network is implemented for the cancer stage classification.

    Paper ID: 13A2G

    Cite this article:

    Sahoo, S., Borugadda, P.K., Lakhsmi, R.(2022). Lung Cancer Nodule Detection by Using Selective Search Feature Extraction and Segmentation Approach of Deep Neural Network. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 13(2), 13A2G, 1-8. http://TUENGR.COM/V13/13A2G.pdf DOI: 10.14456/ITJEMAST.2022.28

References

  1. Zhang, F., Cai, W., Song, Y., Lee, M. Z., Shan, S., & Dagan, D. (2013, July). Overlapping node discovery for improving classification of lung nodules. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5461-5464). IEEE.
  2. Raju, P. D. R., & Neelima, G. (2012). Image segmentation by using histogram thresholding. International Journal of Computer Science Engineering and Technology, 2(1), 776-779.
  3. Tobias, O. J., & Seara, R. (2002). Image segmentation by histogram thresholding using fuzzy sets. IEEE transactions on Image Processing, 11(12), 1457-1465.
  4. Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International journal of computer vision, 59(2), 167-181.
  5. Chaddad, A., & Tanougast, C. (2017). Texture analysis of abnormal cell images for predicting the continuum of colorectal cancer. Analytical Cellular Pathology, 2017.
  6. Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154-171.
  7. Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International journal of computer vision, 59(2), 167-181.
  8. Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Lambin, P. (2019). Data From NSCLC-Radiomics [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI


Other issues:
Vol.12(13)(2021)
Vol.12(12)(2021)
Vol.12(11)(2021)
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