  Diagnosis of fungal infections can rely on microscopic examination, however,
in many cases, it does not allow unambiguous identification of the species 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 treatment is grave in consequences as the mortality rate for
immunosuppressed patients is high. In this paper, we apply machine learning
approach based on deep learning and bag-of-words to classify microscopic images
of various fungi species. Our approach makes the last stage of biochemical
identification redundant, shortening the identification process by 2-3 days and
reducing the cost of the diagnostic examination.
