1. Load images of infected erythrocytes to a computer 2. Extract the RGB features from infected regions of erythrocytes 3. Obtain binary images of infected erythrocytes and Plasmodium parasites using suitable segmentation techniques 4. Determine the following features from the segmented objects;

i. Ratio of the parasite area to area of the infected erythrocyte

ii. The seven moment invariants of both the color and binary images 5. Use the intensity and saturation components of infected erythrocyte to determine the following features

i. R-measure

ii. 3^rd moment

iii. Uniformity

iv. Entropy 6. Form a feature vector from the features extracted above 7. Use the feature vector obtained in 5 above to train a multi-layer neural network to categorize images of infected erythrocytes into their respective life stages.

8. Determine the classification accuracy of the multi-layer artificial neural network.

9. Choose the network that gave the highest degree of classification accuracy and generalization