Improved Classification of Malaria Parasite Stages with Support Vector Machine using Combined Color and Texture Features

Abstract

Malarial is a mosquito born deadly disease that quickly grows from person to person because of the infectious mosquito bite. Knowing accurately the developing stages of parasite is critical for accurate drag selection for early recovery. However, limited study were found that dealt with the automated classification of malaria parasite stages. In this study, a supervised method for classifying malaria parasite stages from microscopy images has been proposed. To achieve the target, this method combines color and texture features with the support vector machine (SVM) classifier. Three texture features and four color features have been considered. An experimental analysis with an unbalanced dataset of 46,978 single-cell thin blood smear images showed the highest classification accuracy (96.9%) of the proposed color-texture feature which exceeds the performance of a recently published method (87.1%).