IDENTIFICATION OF MALARIA DISEASE AND ITS STADIUM BASED ON DIGITAL IMAGE PROCESSING

Abstract

Malaria is one of the public health problems that could cause death, especially in infants, toddlers, pregnantwomen. Malaria is still a health problem not only in Indonesia but also in some countries in the world.Microscopic testing is the gold standard to malaria disease diagnosis. However, the level of accuracydepends on the level of microbiological expertise and experience. Microscope testing also time consumingand requires extensive equipment. This study built a texture of feature extraction and morphology modelthat can be used to identify the type of malaria parasites along with the stadium on the image of a thinblood smear. Samples are a number of preparations that have been given indications of malaria and havegiven a Giemsa staining. Image acquisition process is done by using a digital microscope with 1000 timesof magnification. The process of segmentation used thresholding method of Otsu. Selection feature usedsequential forward selection (SFS). Classification technique used artificial neural network of learningvector quantization (LVQ) with K-fold cross validation to identify patterns of types of parasites and theirlife stages in order to get different types and stages of malaria disease. The results used a combination oftexture with morphological traits to get the values of accuracy.