Main Article Content

Christin Panjaitan
Yoel Panjaitan
Delima Sitanggang Sitanggang
Sri Wahyuni Tarigan


Dengue is a major health problem in tropical and Asia-Pacific regions which typically spreads rapidly in number of infection patients. Knowing that most of the world's population living in risk areas, in order to diagnose and treat the disease, high skilled experts and human resources are needed. However, in some cases human error potentially may occur. Therefore, in this research we developed a model which can diagnose dengue fever disease. This study used blood smear images that were taken under a digital microscope with 400 x magnification specifications by means of image processing techniques such as color transformation, image segmentation, edge detection feature extraction and white blood cells classification. In this study we used white blood cell counting of the role of cell differentiation as a new feature that can classify dengue viral infections of patientsvia decision tree methods. The results showed that, the white blood cells classification modeling technique of 167 cell images resulted in 92.2% accuracy while dengue classification modeling technique of 264 blood cell images resulted in 72.3% accuracy.

Article Details

How to Cite
C. Panjaitan, Y. Panjaitan, D. S. Sitanggang, and S. W. Tarigan, “IMAGE PROCESSING FOR DETECTION OF DENGUE VIRUS”, JUSIKOM PRIMA, vol. 7, no. 2, pp. 26-34, Feb. 2024.


World Health Organization, et al. Dengue: guidelines for diagnosis, treatment, prevention and control. World Health Organization, 2009.

NorachanAuaprasert.Chulalongkorn hematology handbook. Bangkok: chulalongkorn University, 2014.

ChitsanuPunjarean, Darin Sorsotikuland colleagues. Dengue virus diseases: New insightsandtrendsof change.Bangkok: Faculty of Medicine Chulalongkorn University.

SupinunSpeek-Saichua. Color Aids Hematology. Bangkok, 2010.

Sonka, Milan, Vaclav Hlavac, and Roger Boyle. Image processing, analysis, and machine vision. Cengage Learning, 2014.

SaicholSinsombulthong. DATA MINING.Bangkok, 2015.

Mayxay, Mayfong, et al. Predictive diagnostic value of the tourniquet test for diagnosing dengue infection in adults. Tropical Medicine &International Health 16.1, pp. 127-133, 2011.

Katz, Alfred RJ. "Image Analysis and Supervised learning in the automated Differentiation of White Blood cells from Microscopic Images." ,2000.

Joshi, Ms. Minai D., Atul H. Karode, and S. R. Suralkar. "White Blood Cells Segmentation and Classification to Detect Acute Leukemia." International Journal of Emerging Trends & Technology in Computer Science (lJETTCS) 2.3, pp.147-151, 2013.

Madhukar, Monica, Sos Agaian, and Anthony T. Chronopoulos. "New decision support tool for acute lymphoblastic leukemia classification." IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2012.

Parker, Jim R. Algorithms for image processing and computer vision. John Wiley & Sons, 2010.

Russ, John C. The image processing handbook. CRC Press, 20 II.

Bradski, Gary, and Adrian Kaehler. Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc, 2008.

Nixon, Mark. Feature extraction & image processing. Academic Press, 2008.

Pang-Ning Tan. Introduction to Data Mining: Pearson New International Edition. Pearson Education inc, 2014