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Dengue Fever Classification Based on Grey Level Co-occurrence Matrix Feature


Ragini Deshmukh, Dr. Sheshang Degadwala, Arpana Mahajan
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White blood cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body’s usual healing process. White blood cells have potential to divide themselves (through mitosis) to produce more White Blood Cells. Any disease that results in cellular and tissue destruction can potentially be treated by WBC cells. Detection of WBC cells has become an important part in modern medicine to diagnose any disease at its prior onset. But due to their characteristics to change their shape, size and colour at different intervals of time it becomes quite difficult to detect and segment them, as this research is going on to detect WBC cells by using the most efficient algorithm among all that have been studied in the literature survey.

Ragini Deshmukh, Dr. Sheshang Degadwala, Arpana Mahajan

K-means, CMYK-LAB Model, Gray level Co-occurrence Matrix (GLCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN).

  1. J. Poornima and K. Krishnaveni “Detection of Dengue Fever with Platelets Count using Image Processing Techniques” Indian Journals of Science and Technology, Vol 9(19), May 2016
  2.  Zhi Liu 1, Jing Liu “Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering” Sensors 2015, 15, 22561-22586; doi:10.3390/s150922561
  3. SarachTantikitti, SompongTumswadi, WichianPremchaiswadi “Image Processing for Detection of Dengue Virus based on WBC Classification and Decision Tree” Thirteenth International Conference on ICT and Knowledge Engineering 2015
  4. Khan, S., Khan, A., Saleh Khattak, F. and Naseem, A. “An Accurate and Cost Effective Approach to Blood Cell Count” International Journal of Computer Applications, 50(1), pp.18-24. 2012
  5. Viswanathan, P. “Fuzzy C Means Detection of Leukemia Based on Morphological Contour Segmentation” Procedia Computer Science, 58, pp.84-90 2015
  6. Narjes Ghane, AlirezaVard, ArdeshirTalebi, and PardisNematollahy “Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm” J Med Signals Sens. 2017 Apr-Jun; 7(2): 92–101
  7. Safuan, S. N., Tomari, M. R., &Zakaria, W. N. (2017). White Blood Cell (WBC) Counting Analysis in Blood Smear Images Using Various Color Segmentation Methods. Measurement. doi:10.1016/j.measurement.2017.11.002
  8. Ogado, L. H., Veras, R. D., Andrade, A. R., Silva, R. R., Araujo, F. H., & Medeiros, F. N. (2016). Unsupervised Leukemia Cells Segmentation Based on Multi-space Color Channels. 2016 IEEE International Symposium on Multimedia (ISM). doi:10.1109/ism.2016.0103
  9. Robert M. Haralick, K. Shanmugam, And Its'hakDinstein ”Textural Features for Image Classification” IEEE transactions on systems, man, and cybernetics, \o smc(-3, no. 6, November 1973
  10. Gautam, A., &Bhadauria, H.”White blood nucleus extraction using K-Mean clustering and mathematical morphing” 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). doi:10.1109/confluence.2014.694922
  11. Savkare, S. S., &Narote, S. P. ”Blood cell segmentation from microscopic blood images”International Conference on Information Processing (ICIP). doi:10.1109/infop.2015.7489435
  12. Mondal, P. K. “Segmentation of White Blood Cells Using Fuzzy C Means Segmentation Algorithm” IOSR Journal of Computer Engineering, 16(3), 01-05. doi:10.9790/0661-16390105 2014

Publication Details

Published in : Volume 4 | Issue 5 | March-April - 2018
Date of Publication Print ISSN Online ISSN
2018-04-10 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
238-242 CI001   Technoscience Academy

Cite This Article

Ragini Deshmukh, Dr. Sheshang Degadwala, Arpana Mahajan, "Dengue Fever Classification Based on Grey Level Co-occurrence Matrix Feature", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 5, pp.238-242, March-April-2018.
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