Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks

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...