| dc.contributor.author | Nakayenga, Viola | |
| dc.contributor.author | Kaaya, Marvin | |
| dc.contributor.author | Mukwatse, Collin | |
| dc.contributor.author | Miiro, Henry | |
| dc.date.accessioned | 2023-01-30T08:22:01Z | |
| dc.date.available | 2023-01-30T08:22:01Z | |
| dc.date.issued | 2022-09-01 | |
| dc.identifier.citation | Nakayenga,V. et al (2022). Malaria Parasite Detection.(Unpublished dissertation). Makerere University: Kampala | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/15031 | |
| dc.description | A Project Report Submitted to the School of Computing and Informatics Technology
for the Study Leading to a Project in Partial Fulfillment of the Requirements for the Award of the Degree of Bachelor of Science in Software Engineering of Makerere University. | en_US |
| dc.description.abstract | Malaria is a serious global health problem. Its diagnosis is presently 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 at the detection and diagnosis (or screening) of malaria infection in microscopic images of stained thin blood film smears. Thus, the proposed approach combines selected preprocessing, segmentation, feature extraction and edge detection schemes to distinguish malaria cells in order to identify malaria parasites in stained plasmodium images. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Image processing | en_US |
| dc.subject | Malaria detection | en_US |
| dc.title | Malaria Parasite Detection | en_US |
| dc.type | Thesis | en_US |
