Malaria Parasite Detection using Residual Attention U-Net

Abstract

Malaria is a life-threatening disease caused by Plasmodium parasites, and which is still a serious health concern worldwide nowadays. However, it is curable if early diagnosis could be performed. Due to the lack of access to expertise for diagnosis of the disease, often in poorly developed and remote areas, an automated yet accurate diagnostic solution is sought. In Malaysia, there exists 5 types of malaria parasites. As an initial proof of concept, automated segmentation of one of the types, Plasmodium falciparum, on thin blood smear was experimented using our proposed Residual Attention U-net, a type of Convolutional Neural Network that is used in deep learning system. Results showed an accuracy of 0.9687 and precision of 0.9691 when the trained system was used on verified test data.