Deep learning approach to describe and classify fungi microscopic images

PLoS One. 2020 Jun 30;15(6):e0234806. doi: 10.1371/journal.pone.0234806. eCollection 2020.

Abstract

Preliminary 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 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 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 diagnosis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Deep Learning*
  • Fungi / cytology*
  • Image Processing, Computer-Assisted / methods*
  • Microscopy*
  • Support Vector Machine

Grants and funding

This work was supported by the National Science Centre, Poland, under grants no. 2015/19/D/ST6/01215. Two of the authors, Bartosz Zieliński (BZ) and Dawid Rymarczyk (DR), are employed by a commercial company Ardigen. Ardigen provided support in the form of salaries for authors BZ and DR but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the “author contributions” section.