Anopheles project automated malaria detection through processing of erythrocyte images - Manso, Maricon (2007)
The global problem posed by the disease caused by the Malaria parasite Plasmodium accounts for around 1 million deaths annually. Most of these deaths are not due to the absence of a cure but because of the inability to have the disease diagnosed properly, especially in Third World Countries where Malaria is most prevalent. Lack of personnel and facilities greatly attribute to their failure to detect the disease immediately. The researchers' goal is to reduce the necessity to manually examine the erythrocyte images for infection by proving the feasibility of an automated procedure for detecting Malaria Parasitaemia in the images. Written in Java, the researcher's system preprocesses red blood cell (RBC) images by applying various image processing techniques such as grayscale and binary conversion. Extracted hue and saturation values serves as input to the previously trained Artificial Neural Network (ANN). The researchers' system produced a relative 90% accuracy in detecting the Plasmodia-positive pixels in the image, regardless of pathogen stage and species, thus locating the positions of the possible Malaria parasites in the RBC image. The probability of the infection of the examined pixels which is suggested by the researchers' system will be verified with the findings of a credible medical practitioner analyzing the same data set.