  Point of care diagnostics using microscopy and computer vision methods have
been applied to a number of practical problems, and are particularly relevant
to low-income, high disease burden areas. However, this is subject to the
limitations in sensitivity and specificity of the computer vision methods used.
In general, deep learning has recently revolutionised the field of computer
vision, in some cases surpassing human performance for other object recognition
tasks. In this paper, we evaluate the performance of deep convolutional neural
networks on three different microscopy tasks: diagnosis of malaria in thick
blood smears, tuberculosis in sputum samples, and intestinal parasite eggs in
stool samples. In all cases accuracy is very high and substantially better than
an alternative approach more representative of traditional medical imaging
techniques.
