Abstract
Malaria is one of the diseases which is caused by the Parasite Plasmodium, such as Falciparum. This parasite is infected into red blood cells of humans by mosquitoes. The infection can be detected using taking a sample of the red blood cells and testing under the microscope. However, testing can be conducted only by experts, and it needs much time to complete. In this study, we will implement a Randomly Wired Neural Network to detect the parasite falciparum in red blood cells. We have been used 27,558 images of red blood cells as data, which were colored by Giemsa. The result of 5-fold cross-validation using the Randomly Wired Neural Network with four random graphs is better than the method of transfer learning in an average accuracy of 95.08% and 91.99%, respectively.
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