4.3. Results of image interpolation

As a visual comparison of the effect of image interpolation in the spatial and feature domains, Figure 7 shows the result of interpolating from two example red blood cell images.

We conducted various simulations based on the configuration shown in Table 3. For example, we used the augmented images in Dataset 3 to train the LeNet-5 convolutional neural network, and tested the original images in Dataset 2 using the trained network in order to classify the images into two categories: either infected or normal cells. Inversely, we trained the LeNet-5 using the original dataset and tested using the augmented datasets, in order to see how the

Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

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It can be seen in Figure 8 that training and validation using the augmented dataset provides fairly high accuracy (above 90%) when testing using the original dataset, implying the augmented data agree reasonably well statistically with the original data. Besides, feature domain interpolation seems to offer higher accuracy than spatial domain interpolation. Furthermore, the classification accuracies vary more significantly with the interpolation (mixing) coefficient k for spatial domain interpolation than for feature domain interpolation. For both interpolation

(T) and validation (V)
