# Deep learning approach to describe and classify fungi microscopic images

arXiv: https://arxiv.org/abs/2005.11772 · HTML: https://mlsanity.com/paper/2005.11772/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 3 of 3)

- [Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening](https://mlsanity.com/paper/2309.02670/) (2023)
- [Synthetic Fungi Datasets: A Time-Aligned Approach](https://mlsanity.com/paper/2501.02855/) (2025)
- [Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images](https://mlsanity.com/paper/2304.05339/) (2023)

## Abstract (arXiv)

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.