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Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Adetokunbo A. Adenowo, Adesoji A. Awobajo, Sheriff Alimi

Adetokunbo A Adenowo, Adesoji A Awobajo and Sheriff Alimi. Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood. International Journal of Computer Applications 175(12):17-23, August 2020. BibTeX

	author = {Adetokunbo A. Adenowo and Adesoji A. Awobajo and Sheriff Alimi},
	title = {Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2020},
	volume = {175},
	number = {12},
	month = {Aug},
	year = {2020},
	issn = {0975-8887},
	pages = {17-23},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2020920600},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Malaria is a serious global health problem. Its diagnosis is prevalently done manually using conventional compound light microscopy. However, this traditional approach is time consuming, tiresome, gives variation in results and requires skilled personnel which may not be available everywhere and anytime. To overcome these challenges and provide a reliable alternative, a software-based approach is proposed. The approach is underpinned by image analysis techniques; it aims the detection and diagnosis (or screening) of malaria infection in microscopic images of stained thin blood film smears. Thus, the proposed approach combines selected pre-processing, segmentation, feature extraction and edge detection schemes to distinguish malaria cells in order to identify malaria parasites in stained plasmodium images. Ninety-two (92) images of infected and non-infected plasmodium parasites were acquired (in three categories: downloaded, snapped and digitally acquired images), pre-processed, segmented, relevant features extracted and diagnosis made based on features extracted from the images. The accuracy of the approach was tested. Results show that this approach achieved 91.3% accuracy level, 96.6% sensitivity, and 94.4% positive predictive value. The level of outcome suggests that the software approach can be successfully used for malaria detection.


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Algorithm, Feature Extraction, Image Processing, Malaria, Malaria Cell, Parasite Count, Plasmodium, Pre-processing, Segmentation.