  Preliminary diagnosis of fungal infections can rely on microscopic
examination. However, in many cases, it does not allow unambiguous
identification of the species by microbiologist due to their visual similarity.
Therefore, it is usually necessary to use additional biochemical tests. That
involves additional costs and extends the identification process up to 10 days.
Such a delay in the implementation of targeted therapy may be grave in
consequence as the mortality rate for immunosuppressed patients is high. In
this paper, we apply a machine learning approach based on deep neural networks
and Fisher Vector (advanced bag-of-words method) to classify microscopic images
of various fungi species. Our approach has the potential to make the last stage
of biochemical identification redundant, shortening the identification process
by 2-3 days, and reducing the cost of the diagnosis.
