  We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely
AlexNet and VGGNet, for the classification of healthy and malaria-infected
cells in large, grayscale, low quality and low resolution microscopic images,
in the case only a small training set is available. Experimental results
deliver promising results on the path to quick, automatic and precise
classification in unstained images.
