Abstract:
Malaria is a leading cause of deaths globally. Rapid and accurate diagnosis of the disease is a
key to its effective treatment and management. Identification of plasmodium parasites life
stages and species forms part of the diagnosis. In this study, a technique for detecting and
identifying the P. Falciparum parasite life stages of microscopic images of thin blood smears
stained with Giemsa was developed. The technique entailed designing and training Artificial
Neural Network (ANN) classifiers to perform the classification of infected erythrocytes into
their respective stages. Six morphological features were selected for ANN as inputs. These are
Surface roundness, perimeter, maximum diameter, minimum diameter, area and equivalent
diameter. Hence, the neuron numbers of the input layer were six. The output neurons
considered in this study were three that corresponded to the three predefined growth stages of
p. falciparum in malarial RBC considered in this study. The network with 20 hidden layers
was trained by 80% (27 images) of the total data set. Then, the performance of the trained
network was validated by 10% (3 images) and tested by using 10% (3 images) of the total 33
data set. The result of ANN classifier confusion matrix on the morphological features showed
that from the total test examples of 33 images, 32 images (97.0%) were correctly classified
and 1(3.0%) was misclassified
