Classification and quantification of malaria parasites using convolutional neural networks.

dc.contributor.authorAladago, Maxwell Mbabilla
dc.date.accessioned2019-03-08T09:48:23Z
dc.date.available2019-03-08T09:48:23Z
dc.date.issued2018-04
dc.descriptionApplied Thesis submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018.en_US
dc.description.abstractMalaria is currently one of the most deadly diseases in the world. While there are different treatment methods for the disease, the search for new drugs against malaria is still a very important area of research. One of the main challenges in manufacturing drugs against malaria is efficiently evaluating the performance of the drugs on the parasites since it requires, amongst others, precise measurements of the parasite growth-stages as well as their counts in blood smear images. The current gold-standard for making such detail diagnosis is manual microscopy which is tedious. This research showed that convolutional neural networks can be used to identify the different growth-cycle stages of Plasmodium parasites, even in situations where there is little data. Employing a variety of data augmentation techniques and transfer learning, a semantic segmentation model was built to discriminate between trophozoites, gametocytes and normal red blood cells with an accuracy of 85.86% in 353 Giemsa-stained thin blood smears. The results showed that it is possible to perform densepredictionsonGiemsa-stained thin blood smears using convolutional neural networks.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11988/409
dc.language.isoen_USen_US
dc.publisherAshesi Universityen_US
dc.subjectConvolutional neural networksen_US
dc.subjectmachine learningen_US
dc.subjectmalariaen_US
dc.subjectPlasmodium parasitesen_US
dc.titleClassification and quantification of malaria parasites using convolutional neural networks.en_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Aladago_Maxwell_2018_CS_Thesis.pdf
Size:
9.16 MB
Format:
Adobe Portable Document Format
Description: