AUTOMATIC DETECTION OF MALARIA PARASITES FOR ESTIMATING PARASITEMIA

Abstract

Malaria is the leading cause of morbidity and mortality in tropical and subtropical countries. Conventional
microscopy used in diagnosis of the disease has occasionally proved inefficient since it is time consuming and
results are difficult to reproduce. Alternative diagnosis techniques which yield superior results are quite expensive
and hence inaccessible to developing countries where the disease is endemic. In this Project, an accurate, rapid and
affordable model of malaria diagnosis using stained thin blood smear images was developed. The method makes use
of the morphological, colour and texture features of Plasmodium parasites and erythrocytes. Images of infected and
non-infected erythrocytes were acquired, pre-processed, relevant features extracted from them and eventually
diagnosis was made based on the features extracted from the images. Diagnosis entailed detection of Plasmodium
parasites, differentiation of different Plasmodium parasite stages. Image pre-processing entailed reducing the size of
the acquired images to speed up processing and median filtering to remove salt and pepper noise. Neural network
classifiers were then trained and used to detect and determine the life stages and species of Plasmodium parasites.
Classification accuracy of 100%, 92%, 91%, and 85% for detection of infected erythrocytes, stages determination,
respectively was achieved with respect to results obtained by comparing different feature extraction techniques and
different classifiers. In this project we have used three classifiers SVM Naïve bayes and neural network classifier
and two feature extraction techniques DWT and GLCM feature extraction technique. The study revealed that
artificial neural network (ANN) classifiers trained with colour, morphological, and texture features of infected
stained thin blood smear images are suitable for detection and classification of Plasmodium parasites into their
respective stages and species.