Plasmodium parasite detection and species identification on thin blood smears using shape feature analysis and convolutional neural networks - Delas Penas, Kristofer E.; Naval, Prospero C. Jr. (Quezon City College of Engineering, University of the Philippines Diliman, 2017)
Efforts to eliminate malaria would be best served by more effective and efficient diagnosis of cases. The gold standard for malaria diagnosis still remains to be microscopy. However, this process becomes problematic and time-consuming for cases from far-flung areas as experts may not be available in those areas to provide immediate diagnosis. Automation of the diagnostic process with the use of an intelligent System that would recognize Plasmodium parasites could help address this problem. This research proposes such an intelligent System, detecting Plasmodium parasites and the specific species On images of thin blood Smears using convolutional neural networks. The model in this research obtained an accuracy of 93.60 percent and sensitivity of 95.06 percent in detecting Plasmodium parasites, and an accuracy of 89.27 percent in identifying the species Plasmodium falciparum and Plasmodium vival. The model's accuracy is comparable to the 90 percent accuracy of human validators of malaria cases in identifying malaria, making it an effective tool to automate the diagnostic process.
Thesis (M.S. Computer Science)--University of the Philippines Diliman
Keywords: Neural networks (Computer science); Plasmodium; Malaria; Image processing -- Digital techniques; Imaging systems in medicine