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

Authors(3) :-Ragini Deshmukh, Dr. Sheshang Degadwala, Arpana Mahajan

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.

Authors and Affiliations

Ragini Deshmukh
ME Student Computer Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
Dr. Sheshang Degadwala
Head of Department Computer Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
Arpana Mahajan
Assistant Professor Computer Department, Sigma Institute of Engineering, Vadodara, Gujarat, India

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

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Publication Details

Published in : Volume 4 | Issue 5 | March-April 2018
Date of Publication : 2018-04-10
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 238-242
Manuscript Number : CI001
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

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
URL : http://ijsrset.com/CI001

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