  We apply convolutional neural networks to identify between malaria infected
and non-infected segmented cells from the thin blood smear slide images. We
optimize our model to find over 95% accuracy in malaria cell detection. We also
apply Canny image processing to reduce training file size while maintaining
comparable accuracy (~ 94%).
