Plasmodium vivax classification from digitalization microscopic thick blood film using combination of second order statistical feature extraction and K-Nearest Neighbor (K-NN) classifier method

Malaria disease is one of the most serious public health problem in various tropical countries, included in Indonesia. Data of The Ministry of Health mentioned that Papua, West Papua, and NTT (Nusa Tenggara Timur) are provinces which have the greatest cases of malaria. With high mortality rate, malaria need to be treated as quick as possible. Therefore, the accurate and timely diagnosis of malaria infection is essential to control and to cure the disease. We propose an accurate method to classify plasmodium vivax from digitalization microscopic thick blood film using combination of second order statistic feature extraction and K-Nearest Neighbor (K-NN) classifier method. In this feature extraction, we use GLCM (Gray Level Co-occurrence Matrix) to get contrasts, correlations, energys, and homogeneity values. Those values will be inserted in classification module as an input. We use K-NN classifier method to classify the red blood film are infected by plasmodium vivax or not. This process can also classify plasmodium vivax into thropozoit, schizont, and gametocytes. Based on the result of experiments, the combination of second order statistical and K-NN has a high accuracy for classifying plasmodium vivax with average accuracy 95%.