Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained experts to interpret the data.
In this work, a two stage automated image classification strategy is proposed, to eliminate this dependency on human expertise. Blood slides that were photographed at 20 X magnification were used in our experiments, allowing for a larger Field of View than regular thin film microscopy at 100 X.
Erythrocytes are first localised and segmented by a Convolutional Neural Network, the architecture of which is based on U-Net, with some adaptations and improvements made for our purposes. The sensitivity and positive predictive value of the localisation were both 0.998, resulting in accurate cell counts.
A transfer learning strategy, in which the existing VGG-16 network is used as a feature extractor and combined with a new fully connected layer to predict correct activations for our classification, is then used to classify the segmented erythrocytes as either infected with Plasmodium Falciparum parasite or healthy. Sensitivity and specificity of the predicted classification were 0.795 and 0.915 respectively.
It is concluded that, although this method may not fully eliminate the need for trained experts, the algorithms proposed can be of great assistance in aiding the diagnostic decision making process.