# Deep learning approach to description and classification of fungi microscopic images

## TL;DR

This paper presents a deep learning method that classifies fungi microscopic images, reducing diagnosis time and costs by eliminating the need for additional biochemical tests, thus enabling faster and cheaper fungal identification.

## Contribution

The paper introduces a novel deep learning and bag-of-words approach for fungi image classification, streamlining diagnosis and reducing reliance on biochemical tests.

## Key findings

- Reduces diagnosis time by 2-3 days
- Decreases diagnostic costs
- Achieves accurate fungi species classification

## Abstract

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.