Deep learning approach to describe and classify fungi microscopic images
Open accessAbstract
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.
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Dependence on special stains meant there was less potential of shortening turnaround times and reducing the use of resources.
“Additionally, the dependence on special stains meant there was less potential of shortening turnaround times and reducing the use of resources [26,27,28,29,30,31,32].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [31]
The Materials section says the microscopic preparations were Gram-stained, and the Introduction and Abstract say the proposed method could shorten diagnosis and reduce costs by replacing biochemical tests. The paper does not say that dependence on staining limited the potential to save time or resources. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThere is potential for using AI models for the detection of fungal organisms, as already reported in other fields of histopathology.
“Hence, there is potential for using AI models for the detection of fungal organisms, as already reported in other fields of histopathology [27,28,29,30,31].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [31]
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References 33 · Cited by 126
Research works printed but not linked: 1
- [31]Nieto D., Brill A., Kim B., Humensky T.B. Exploring deep learning as an event classification method for the Cherenkov Telescope Array. arXiv preprint arXiv:1709.05889 (2017). · arXiv
- Authors
- Zieliński, B., Sroka-Oleksiak, A., Rymarczyk, D., Piekarczyk, A., & Brzychczy-Włoch, M.
- Affiliations
- Jagiellonian University (PL)
- Corresponding author
- Zieliński, Bartosz
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-06-30
- Volume
- 15
- Issue
- 6
- Pages
- e0234806
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7326179
- OpenAlex
- W3027782394
- Cited by
- 126
- References
- 33
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 27 · 2023: 22 · 2022: 15 · 2021: 9 · 2020: 6
- FWCI
- 6.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- arXiv (Cornell University)
- Medical Imaging with Deep Learning
- Funders
- Narodowe Centrum Nauki (2015/19/D/ST6/01215)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- fungal identification, fungal microscopy, deep neural network, bag-of-words, image classification, fungal infection, biochemical identification, delayed diagnosis, immunocompromised patients
- MeSH terms
- Deep Learning, Fungi, Image Processing, Computer-Assisted, Microscopy, Support Vector Machine
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