1. Load RGB images with different life stages ofPlasmodium parasites 2. Convert the image into double class.

3. Extract the red, green, and blue intensity pixel values from images of infected erythrocytes. These features should be obtained from infected regions of erythrocytes 4. Form feature vectors comprising of three elements using the three colour components extracted in step 3 5. Categorize these feature vectors into four classes; early trophozoites, mature trophozoite, schizonts, and gametocytes stages.

6. Form the corresponding target (desired output) vector for feature classes of step 5. The four target vectors for the four classes were, [1 0 0 0]^T, [0 1 0 0]^T, [0 0 1 0]^T, [0 0 0 1]^T where T denotes matrix transpose.

7. Train a multilayer neural network with varying numbers of hidden neurons and record learning accuracies.

8. Choose the ANN with highest degree of classification accuracy and generalization